Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Abstract

Outdoor vision-based systems suffer from atmospheric turbulences, and rain is one of the worst factors for vision degradation. Current rain removal methods show limitations either for complex dynamic scenes, or under torrential rain with opaque occlusions. We propose a novel derain framework which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust against rain occlusion interferences and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for the location of rain streaks and the occluded background contents. Different classical and novel methods such as Robust Principle Component Analysis and Convolutional Neural Networks are applied and compared for their respective advantages in efficiently exploiting the rich spatial-temporal features provided by the two tensors. Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes. Visual evaluations show that the proposed framework is not only able to suppress heavy and opaque occluding rain streaks, but also large semi-transparent regional fluctuations and distortions.

superpixel segmentation, rain removal, dynamic content, spatial-temporal filtering, robust principle component analysis, convolutional neural networks.

1 Introduction

As modern intelligent systems rely more and more on visual information, the robustness of the system becomes dependent on the quality of visual input. Outdoor vision modules, in particular, are susceptible to visual degradations caused by atmospheric turbulences. Turbulent weather conditions can be generally classified into steady or dynamic systems [1, 2]. For steady systems, such as fog and haze, small (1-10 ) atmospheric particles suspended in the air induce accumulated scattering of scene radiance; hence, they primarily degrade visual contrast and visibility. For dynamic systems, such as rain and snow, the atmospheric particles are much larger (0.1-10 ) and relatively fast-moving. As such, the motions of individual or aggregate particles become visible and cause complex visual distortions. These distortions could unintentionally introduce motion content or obscure visual features, which negatively impacts computer vision algorithms. In this paper, we propose a rain removal framework that handles both the dynamic and steady state visual degradations of rain.

To devise a rain removal algorithm, we should first understand the visual effects caused by rain. Rain appearances can be significantly different depending on scene distance ranges and imaging configurations [3]. Based on their visual effects, we classify rain into three types. As shown in Fig. 1, these are:

  • Occluding rain: these raindrops fall within the depth of field of a camera. They are in focus, and fall at high speed. Hence, they appear as slender streaks under normal camera exposure settings (shutter speed slower than 1/200 second). In general, these streaks are somewhat opaque and occlude the background scene.

  • Veiling rain: raindrops that fall nearer than the current depth of field cover a larger field of view and are out of focus. They might not take distinct shapes and resemble semi-transparent veils, causing regional intensity fluctuations and distortions.

  • Accumulated rain: raindrops distributed further than the current depth of field are out of focus, cover a tiny field of view, and are distributed over large distances. Their visual effects are accumulated over many samples and are stable over time, producing visual degradations similar to that of fog and haze.

Fig. 1: Different photometric appearances of raindrops at different distance range and camera configurations.

Based on the types of input, we can categorize rain removal methods into image-based methods and video-based methods. Image-based methods rely only on information from the processed frame while video-based methods have access to other frames in the sequence. Most video-based methods rely primarily on temporal information since they are very discriminative for rain detection – both occluding rain and veiling rain produce intensity fluctuations over time; other clues like rain structure might not consistently hold under all rainy conditions, e.g., under veiling rain.

However, temporal information are reliable only if, for each pixel, the corresponding scene content is consistent over time. This constraint could be violated due to two factors – motion of the camera and presence of moving objects. Previous works tackle these two issues separately. Camera motion-induced scene content shifts were reversed using global frame alignment [4, 5, 6]. However, the granularity of global alignment is too large when scene depth range is large; to be aligned accurately, scene content of different depths should be independently aligned. Thus, by using global alignment, parts of the scene could be poorly aligned.

The presence of moving objects disrupts temporal consistency of scene content, and could thus cause scene elements to be misclassified as rain. Earlier works attempt to identify the misclassified scene pixels by testing various properties of candidate rain pixels, such as photometric properties [7], chroma changes [8], geometric properties [9] or local statistics [4]. This approach, however, does not adequately remove rain that overlap moving objects. Later works first segment out motion regions or foreground objects before applying rain removal on these regions separately [10, 6, 11]. However, these methods rely on accurate segmentation of foreground regions.

We recognize that both camera motion and moving objects cause scene content to be misaligned and thus corrupt temporal information. Hence, in this paper, we propose a novel and elegant framework that simultaneously solves both issues by aligning scene content to compensate for their misalignment – superpixel-based content alignment and compensation (SPAC) for rain removal. First, the target video frame is segmented into superpixels (SP)s; each SP corresponds to a depth consistent patch of scene content. Then, for each target SP, we form tensors by stacking patches similar to the target SP. This step aligns content corresponding to both scene background and moving objects without prior segmentation of the moving object. Scene content is also much better aligned at a SP-level granularity. Finally, a de-rained SP patch is extracted from the tensors. Within this framework, we experimented on several ways to utilize these tensors for rain removal – taking the average values of the tensor (SPAC-Avg and SPAC-) , applying robust principle analysis (SPAC-RPCA), and processing it using a Convolutional Neural Network (SPAC-CNN).

Extensive evaluations show that up to 5dB reconstruction PSNR advantage is achieved over state-of-the-art methods, and the advantage is especially obvious in highly dynamic scenes taken from a fast moving camera. Visual evaluations show that the proposed framework is not only able to remove heavy and opaque occluding rain streaks, but also suppress large semi-transparent regional fluctuations and distortions. Fig. 2 illustrates the advantage of our proposed algorithm over existing methods in a challenging video sequence. The contributions of this work can be generalized as follows.

  • We propose to apply spatial-temporal alignment on scene content for video-based rain removal. This approach, ensures content consistency over consecutive frames whether subjected to camera motion or presence of moving objects. Hence, segmentation of moving objects for separate treatment is not required.

  • We propose to use Superpixels as the basic unit for scene content alignment. Alignment of small blocks of pixels is not robust to structured noise, especially large rain streaks. Large blocks of pixels, however, are oblivious to object boundaries, and might encompass disjointed content located at different depths. By using SPs, we can form sufficiently large clusters of pixels that also respect object boundaries.

  • We tested a set of different rain removal methods on aligned scene content. In particular, we propose a neural network-based (SPAC-CNN) method where a set of efficient spatial-temporal features is used to help restore high frequency details lost during the de-raining process.

  • As far as we know, this is the first work to categorize testing rain data based on camera speed for separate evaluations. A rain video dataset was created with a car-mount camera with speed range between 0 to 30 km/h over different dynamic scenes. The data set was used both for model training and evaluations.

(a)
Fig. 2: Comparison of derain outputs by different algorithms for a challenging video sequence with fast camera motion and heavy rain fall. Image-based derain methods, i.e., discriminative sparse coding (DSC) [12] and deep detail network (DDN) [13] fail to remove large and/or opaque rain streaks. Video-based derain methods, i.e., via matrix decomposition (VMD) [6], and via temporal correlation and low-rank completion (TCL) [14], create serious blur due to fast camera motion, and also show significant amount of remnant rain streaks. Our proposed SPAC-CNN can cleanly remove the rain streaks and preserve scene content truthfully.

A shorter version of this work appeared in [15]. This journal version presents more explanations and technical details, and includes additional experiments to verify the efficiency of using superpixels over blocks. We have also included additional rain removal methods, SPAC- and SPAC-RPCA, under our superpixel-based content alignment framework. Comparisons to additional competing video-based methods have also been added. A patent for an implementation based on SPAC- [16] is being filed in Singapore.

The remainder of the paper is organized as follows. In Section 2, we will discuss the current rain removal methods. Then, in Section 3, we introduce our proposed framework and methods in detail. In Section 4, we show comprehensive evaluations of our methods and competing methods, and discuss the results. Finally, Section 5 concludes the paper.

2 Related Work

Rain can exhibit dynamic effects and steady state effects. Steady state rain, which we call accumulated rain, can be removed using haze and fog removal methods [17, 18, 19]. For dynamic rain, rain removal methods are usually categorized into video-based and image-based methods. Alternatively, since rain appearance is a function of camera parameters, it is possible to reduce the visibility of rain by adjusting these parameters [3]. Another related research topic is adherent rain removal [20, 21], which deals with distortions caused by raindrops that adhere on a transparent surface.

2.1 Image-based methods

Rain removal based on a single image is usually modeled as a layer separation problem, where the input image is a composition of a background layer and rain layer. In order to separate both layers, priors intrinsic to background and rain respectively are required. Kang et al. [22] proposed to learn a dictionary from the high frequency layer, which contains both background edges and rain streaks, of the input image. By assuming that rain streaks are of similar orientation throughout the image while background edges are not, atoms corresponding to rain and background are separated based on their Histogram of Gradients. The high frequency layer of the output is obtained by applying sparse coding using the background dictionary. Further enhancements to this framework include improving the separation of rain and background atoms [23, 24], and considering additional priors such as blurriness and saturation of rain [25].

Chen and Hsu [26] derived an optimization function where that the rain layer is assumed to be low ranked while the background layer varies smoothly. Luo et al. [12] relied on discriminative sparse coding, where their rain dictionary is trained from simulated rain. Li et al. [27, 28] modeled rain and background layers using Gaussian mixture models (GMM). Natural images are used to train the background GMM while pure rain streak regions in the input, which do not contain background features, are used for training the rain GMM. Instead of applying layer separation, Kim et al.[29] identified rain pixels using local statistical and geometric properties, such as orientation, shape and brightness, and then recovered their values using non-local means filtering.

Convolutional Neural Networks have been very effective in both high-level vision tasks [30] and low-level vision applications for capturing signal characteristics [31, 32]. Different network structures and features were explored for rain removal. Fu et al. proposed the deep detail network [13] which uses a priori image domain knowledge to focus on learning high frequency details of the rain and differentiating them from interferences of background structures. Yang et al. [33] proposed a multi-task deep learning architecture that involves learning of the binary rain streak map, the appearance of rain streaks, and the clean background under one framework. These CNN structures still shown limitations for opaque and heavy rain occlusions.

Fig. 3: System diagram for the proposed SPAC framework. The switch changes algorithms between SPAC-Avg, SPAC-, SPAC-RPCA, and SPAC-CNN.

2.2 Video-based methods

Most video-based methods rely primarily on temporal analysis of pixel values. In principle, the intensity of each pixel is analyzed over time; elevated intensity values indicate the presence of rain. These rain pixels are then restored using pixel values from neighboring frames. The issue with temporal analysis is that camera motion and dynamic scene motion could also cause intensity spikes, creating false positives. Thus, methods have been explored to identify these false positives from rain-induced spikes.

For example, Garg and Nayar [7, 2] proposed to detect candidate rain pixels by thresholding intensity differences between the target frame and its two immediate neighbors. True rain pixels are differentiated from false positives by comparing intensity changes to background intensities, and further refined by comparing rain patterns with those of neighboring frames. The rain pixel is then restored from the average of the two neighboring frames. Later works proposed different methods to identify true rain pixels from false positives. Zhang et al. [8] checked whether intensity changes of RGB color channels are similar, Bossu et al. [9] applied geometric constraints, such as size and orientation, to candidate rain streaks, and Tripathi and Mukhopadhyay [4] modeled intensity fluctuations in a local spatio-temporal area. In general, when rain pixels overlap with moving objects, which can generate false positives, this approach does not perform well. Chen and Chau [10] proposed a solution by segmenting motion regions and de-raining them with alternative approaches.

More recently, Jiang et al. [34] decomposed a video into background and rain layers by enforcing a set of priors, such as low-rankedness of background, sparsity of rain, and total variation of rain and background in different directions. Wei et al. [11] decomposed a video into background, rain, and foreground (moving objects) layers by modeling the rain layer as patch-based mixture of Gaussians, background layer as a low rank structure, and foreground as a sparse and spatial-temporally continuous structure. However, the assumption that the background is low-ranked is invalidated when the camera is in motion. Ren et al. [6] made a similar matrix decomposition, where background is constrained to be low-ranked, and foreground and rain layers are constrained to be sparse. Moving objects are de-rained by temporally aligning them using patch matching, while the moving camera effect is modeled using global frame transform. Although global frame transform can compensate for camera motion, it is inadequate when camera motion is fast or when the scene contains large depth ranges. Furthermore, a relatively large number of frames is required for low rank decomposition, which makes their alignment difficult to achieve under fast camera motion.

Kim et al. [14] estimated a hybrid warped frame from two immediate neighbors using optical flow. This frame, assumed to be rain-free, is compared with the target frame to detect rain candidates, which are then refined using sparse coding. Rain pixels are recovered using block-based matrix completion. There are also works with less emphasis on using temporal data. Barnum et al. [35] modeled rain streaks in frequency space, Santhaseelan and Asari[36] use phase congruency on frame difference images to detect candidate rain streaks, and Kim et al. [37] applied horizontal directional thresholding to detect candidate rain streaks.

In general, video-based methods potentially perform better than image-based methods, since temporal information is available. However, unless accounted for, camera motion and scene object motion invalidate temporal information and thus affect performance. Image-based methods, as do some video-based methods, rely on priors or assumptions about properties of rain. However, as shown in our real world experimental videos, rain could appear in irregular sizes, shapes, brightness, opacity, or distributions; these rain are not easily removed by existing image-based methods.

3 Proposed Model

Throughout the paper, scalars are denoted by italic lower-case letters, 2D matrices by upper-case letters, and 3D tensors and functions by script letters.

Fig. 3 shows the system diagram for the proposed rain removal framework based on SuperPixel content Alignment and Compensation (SPAC). The framework can be divided into two parts: first, video content alignment is carried out based on SP units. For each SP, we perform two SP template matching operations, which produce two output tensors: the optimal temporal match tensor , and the sorted spatial-temporal match tensor . Second, rain removal and scene content compensation are carried out over the slices1 of the two tensors and . Various classical and novel methods such as Robust Principle Component Analysis [39] (RPCA) and Convolutional Neural Networks [30] (CNN), will be investigated and compared under our proposed SPAC framework [15] for their respective advantages in the task of scene content restoration.

3.1 Robust Content Alignment via Superpixel Spatial-Temporal Matching

Given a target frame of a rainy video, we consider its immediate past and future neighbor frames to create a sliding buffer window of length : . Here, negative and positive indicate past and future frames, respectively. We only derain the Y luminance channel. The derain output is used to update the corresponding frame in the buffer to improve rain removal of future target frames (As shown Fig. 3). This history buffer update mechanism contributes to cleaner derain output especially under heavy rainfall scenarios.

One of the most important procedures for video-based derain algorithms are the estimation of content correspondence between video frames. With accurate content alignment, rain occlusions could be easily detected and removed with information from the temporal axis. The alignment, however, is no trivial task. Camera motion changes the scene significantly, and the change of perspective causes scene contents at different distance to shift at different parallax. Additionally, scene contents could have their own rigid/non-rigid motion, not to mention the serious interference caused by rain streak occlusions. All these factors make content alignment extremely challenging for rainy videos.

Content Alignment: Global vs. Superpixel

Fig. 4: Motivation of choosing SP as basic processing unit. (c) shows the outcome of geometrical alignment between (a) and (b). (d) and (e) compare segmentation based on rectangular and SP units.

The popular solution to compensate camera motion between two frames is via a homography transform matrix estimated based on global consensus of a group of matched feature points [40, 41]. Due to the reasons analyzed in Sec. 1, perfect content alignment can rarely be achieved for all pixels with a global transform at whole frame level, especially for dynamic scenes with large depth range. To illustrate, two frames in Fig. 4(a) and (b) are geometrically aligned via global homography transform, and the aligned frames are overlapped and shown in Fig. 4(c). It can be seen that scene contents at far and middle distance ranges are well-aligned; however, for nearer contents (e.g., the banisters), obvious mis-alignment appear.

The solution naturally turns to pixel-level alignment, which faces no fewer challenges: first, feature points are sparse, and feature-less regions are difficult to align; more importantly, rain streak occlusions will cause serious interferences to feature matching at single pixel level. Contextual information from neighboring area is indispensable to overcome rain interferences. This leads us to our solution: to decompose images into smaller depth consistent units.

The concept of SuperPixel (SP) is to group pixels into perceptually meaningful atomic regions [42, 43, 44]. Boundaries of SP usually coincide with those of the scene contents. Comparing Fig. 4(d) and (e), the SPs are very adaptive in shape, and are more likely to segment uniform depth regions compared with rectangular units. We adopt SP as the basic unit for content alignment.

Optimal Temporal Matching for Rain Detection

Let denote the set of pixels that belong to the -th SP on . Let be the bounding box that covers all pixels in (). Let denote a spatial-temporal buffer centered on . As illustrated in Fig. 3, the temporal range of spans the entire sliding buffer window, and its spatial range is set to cover the possible motion range of in its neighboring frames.

Pixels within the same SP are very likely to belong to the same object and show identical motion between adjacent frames. Therefore, we can approximate the SP’s appearance in other frames based on its appearance in the current frame via linear translations.

Searching for the reference SP is done by template matching of the target SP at all candidate locations in . A match location is found at frame according to:

(1)

As shown in Fig. 5(d), indicates SP pixels in the bounding box . denotes element-wise multiplication. Each match at a different frame becomes a temporal slice for the optimal temporal match tensor :

(2)
Fig. 5: Illustration of various masks and matching templates used in the proposed algorithm.

Based on the temporal clues provided by , a rain mask can be estimated. Since rain increases the intensity of its covered pixels [7], rain pixels in are expected to have higher intensity than their collocated temporal neighbors in . We first compute a binary tensor to detect positive temporal fluctuations:

(3)

where operator is defined as replicating the 2D slices times and stacking along the thrid dimension into a tensor of . To robustly handle re-occurring rain streaks, we classify pixels as rain when at least 3 positive fluctuations are detected in . An initial rain mask can be calculated as:

(4)

Due to possible content mis-alignment, edges of background could be misclassified as rain. Since rain steaks don’t affect values in the chroma channels (Cb and Cr), a rain-free edge map could be calculated by thresholding the sum of gradients of these two channels with . The final rain mask is calculated as:

(5)

A conceptual demonstration of , , and is shown in Fig. 5(a), (b), and (c), respectively. In our implementation, is set to while is set to .

Sorted Spatial-Temporal Template Matching for Rain Occlusion Suppression

A second round of template matching will be carried out based on the following cost function:

(6)

The rain-free matching template is calculated as:

(7)

As shown in Fig. 5(e), only the rain-free background SP pixels will be used for matching. Candidate locations in (except current frame ) are sorted in ascending order based on their cost defined in Eq. (6). The top candidates with smallest will be stacked as slices to form the sorted spatial-temporal match tensor . With masked template matching eliminating the interferences from rain, the slices of are expected to be well-aligned to the current target SP. Since rain pixels are temporally randomly and sparsely distributed within , when is sufficiently large, we can get a good estimation of the rain free image through tensor slice averaging, which functions to suppress rain induced intensity fluctuations, and recover the occluded background pixels:

(8)

If we only extract one tensor slice from each frame as done for , there will be too few samples for suppression of heavy rain. For , we set ; thus, contains corresponding scene content from neighboring frames along with their slightly shifted copies. Hence, tensor averaging is similar to weighted spatio-temporal averaging of aligned scene content, where the binary weights are dependent on similarity to the target patch. We denote this method as SPAC-Avg, and use it as the baseline method in our SPAC framework. Fig.6 illustrates the steps involved in computing SPAC-Avg.

Fig. 6: Illustration of calculation flow for SPAC-Avg and feature preparation for SPAC-CNN.

3.2 Rain Removal and Low Rank Content Modeling via Robust Principle Component Analysis

As shown in experimental results, although SPAC-Avg proves to be very effective in suppressing rain, it also blurs background details. Hence, in this section, we attempt to use a more advanced mathematical model – Robust Principle Component Analysis (RPCA) [45].

RPCA was originally proposed to recover corrupted entries from data, where the collection of data is low ranked and the corrupted entries are sparse. However, it has been successfully applied to many other applications, such as [46, 47, 48]. Its main advantage over averaging-based methods is that it is robust to corruptions of large magnitudes.

Let denote the operator that extracts the pixels of a target SP from a 2D rectangular patch, and then stacks them as a vector . Here, denotes the number of pixels in the target SP. A 2D matrix can be formed from the target SP and slices in :

(9)

Should the SP contents in be aligned, columns of will be similar with each other, and should be low-rank. We model the rain-free content of as a low-rank matrix ; the rain occlusions and small shifts typically affect only a small fraction of pixels in an image, therefore we model them as a sparse matrix .

Finally, the rain removal problem can now be expressed as:

(10)

Here, the -norm counts the number of nonzero entries in the matrix . is a parameter that trades off the rank of versus the sparsity of .

The problem in Eq. (10) is highly nonconvex due to the calculation of matrix rank and -norm. As suggested in [45], we relax it to its convex surrogate as:

(11)

which replaces rank with the nuclear norm of (which equals the sum of its singular values), and replaces the -norm with the -norm: . It has been shown in [45] that Eq. (11) can exactly recover in Eq. (10) as long as long as the rank of the matrix is not too high and the number of nonzero entries in is not too large. This condition is generally met when the alignment quality of is high, and when the rain occlusions are not too dense.

The convex problem in Eq. (11) can be efficiently solved via modern first-order optimization techniques such as the Augmented Lagrange Multiplier (ALM) algorithm [49]. The derained output SP is extracted from the first column of low-rank matrix using the inverse of . We denote this method as SPAC-RPCA.

3.3 Rain Removal and Content Compensation in a CNN framework

As to be shown in Sec. 4, when camera motion gets faster (which leads to larger mis-alignment errors), and when rain occlusions get dense, SPAC-RPCA shows limitations. Under both circumstances, the low-rank assumption of , and the sparseness assumption of are violated.

We further investigate another solution for this challenging scenario with a CNN model, which is expected to have a better capacity to robustly model the rain-free background contents with its deep and highly non-linear layers. In this section, we aim at designing a CNN structure (denoted as SPAC-CNN) for restoring the lost high frequency scene content details back to , however without re-introducing the rain streaks (rain streaks are also high frequency signals). We will first introduce some informative features for this task, then we proceed to the network structure design details.

Occluded Background Feature

from Eq. (8) can be used as one important clue to recover rain occluded pixels. Rain streak pixels indicated by the rain mask are replaced with corresponding pixels from to form the first feature :

(12)

Feature itself is already a good derain output, which we denote as SPAC-. However its quality is greatly limited by the correctness of the rain mask . For false positive2 rain pixels, will introduce content detail loss; for false negative pixels, rain streaks will be added back from . This calls for more informative features for the CNN.

Temporal Consistency Feature

The temporal consistency feature is designed to handle false negative rain pixels in , which falsely add rain streaks back to . For a correctly classified and recovered pixel (a.k.a. true positive) in Eq. (12), intensity consistency should hold such that for the collocated pixels in the neighboring frames, there are only positive intensity fluctuations caused by rain in those frames. Any obvious negative intensity drop along the temporal axis is a strong indication that such pixel is a false negative pixel.

The temporal slices in establishes optimal temporal correspondence at each frame, which embeds enough information for the CNN to deduce the above analyzed false negative logic, therefore they shall serve as the second feature :

(13)

High Frequency Detail Feature

The matched slices in are sorted according to their rain-free resemblance to , which provide good reference to the content details with supposedly small mis-alignment. We directly use the tensor as the last group of features . This feature will compensate the detail loss introduced by the operations in Eq. (12) for false positive rain pixels.

In order to facilitate the network training, we limit the mapping range between the input features and regression output by removing the low frequency component () from these input features. Pixels in but outside of the SP is masked out with :

(14)

The final input feature set is . The feature preparation process for SPAC-CNN is summarized in Fig. 6.

CNN Structure and Training Details

Fig. 7: CNN architecture for compensation of mis-alignment blur. Each convolutional layer is followed by a rectified linear unit (ReLU).

(a)
Fig. 8: Visual comparison of reconstructed central views for the stereo data drumsticks and dwarves from the Middlebury Stereo Dataset [50], based on matched rectangular patches, and SP units. Second row show zoom-in details of highlighted regions of the respective first row images.

The CNN architecture is designed as shown in Fig. 7. The network consists of four convolutional layers with decreasing kernel sizes of 11, 5, 3, and 1. All layers are followed by a rectified linear unit (ReLU). Our experiments show this fully convolutional network is capable of extracting useful information from the input features and efficiently providing reliable predictions of the content detail . The final rain removal output will be:

(15)

For the CNN training, we minimize the distance between the derain output and the ground truth scene:

(16)

here denotes the ground truth clean image. We use stochastic gradient descent (SGD) to minimize the objective function. Mini-batch size is set as 50 for better trade-off between speed and convergence. The Xavier approach [51] is used for network initialization, and the ADAM solver [52] is adpatoed for system training, with parameter settings 0.9, 0.999, and learning rate 0.0001.

To create the training rain dataset, we first took a set of 8 rain-free VGA resolution video clips of various city and natural scenes. The camera motions are diverse for the dataset, e.g., panning slowly with unstable movements, or mounted on a fast moving vehicle with speed up to 30 km/h. Next, rain was synthesized over these video clips with the commercial editing software Adobe After Effects [53], which can create realistic synthetic rain effect for videos with adjustable parameters such as raindrop size, opacity, scene depth, wind direction, and camera shutter speed. This provides us a diverse rain visual appearances for the network training.

We synthesized 3 to 4 different rain appearances with different synthetic parameters over each video clip, which provides us 25 rainy scenes. For each scene, 21 frames were randomly extracted (together with their immediate buffer window for calculating features). Each scene was segmented into approximately 300 SPs, therefore finally we have around 157,500 patches in the training dataset.

4 Performance Evaluation

In this section, we evaluate the functional components as well as overall derain performance of the methods under the proposed SPAC framework, and compare them with recent state-of-the-art rain removal methods.

Each frame was segmented into around 300 SPs using the SLIC method [42]. For a VGA resolution frame, this results in average bounding box sizes of . By considering two previous and two future neighbor frames, the spatial-temporal buffer dimension was 30305. The length of tensor , , was set to 10.

For SPAC-RPCA, is set to , which we found to be able to achieve a good balance between rain removal and background detail preservation. For SPAC-CNN, MatConvNet [54] was adopted for model training, which took approximately 54 hours to converge over the training dataset introduced in Sec. 3.3.4. The training and all subsequent experiments were carried out on a desktop with Intel E5-2650 CPU, 56GB RAM, and NVIDIA GeForce GTX 1070 GPU.

All the image and video results reported in this section can be accessed online3.

4.1 SP Matching Efficiency Evaluation

Data block Matching PSNR SP Matching PSNR SP Matching Advantage
Art 30.68 32.18 +1.51
Books 34.74 35.83 +1.09
Computer 34.69 36.40 +1.71
Dolls 33.02 34.50 +1.48
Drumsticks 29.09 30.96 +1.87
Dwarves 35.55 36.80 +1.25
Laundry 33.03 33.75 +0.72
Moebius 34.05 35.35 +1.30
Reindeer 35.15 35.42 +0.27
Average 33.33 34.58 +1.24
TABLE I: Central view reconstruction PSNR (dB) based on six side views for the Middlebury Stereo Dataset [50].

For all methods under the proposed SPAC framework, the choice of SP as the basic operation unit is key to their performances. When other decomposition units are used instead (e.g., rectangular patches), matching accuracy deteriorates, and very obvious averaging blur will be introduced especially at object boundaries.

We quantitatively evaluate the advantage of content alignment based on SP units compared with rectangular units over the Middlebury Stereo Dataset [50]. The dataset contains 9 image sequences, with each looking at a target scene from 7 different viewing angles. The baselines between the angles are large enough to simulate fast camera motion.

A straightforward reconstruction method is used for this evaluation. The 7 views are treated as 7 consecutive video frames and forms a 7-frame buffer. For a certain target matching unit (either SP or rectangular block) on the central view (i.e., the 4th frame), we find a best match in the spatial-temporal buffer with the smallest matching error defined in Eq. (6). Note that in this experiment, is set equal to the SP mask , since there is no rain in this dataset; and for rectangular patches, the mask is directly set to all ones. An approximated central view will be reconstructed using their optimal spatial-temporal matches from the six side views (i.e., frame 1,2,3,5,6,7 of the sequence).

The approximation quality measured in PSNR for each data is shown in TABLE I. As can be seen, matching based on SP units provides an average 1.24 dB advantage over those based on rectangular patches. Fig. 8 shows the visual comparison of reconstructed central views between the two methods. As can be seen, SP provides better approximation especially along occluding boundaries. Reconstruction based on rectangular patches show obvious artifacts at these regions. This experiment validates the advantage of using SP units for content alignment between adjacent frames.

4.2 Rain Removal Quantitative Evaluations

To quantitatively evaluate our proposed algorithms, we used a set of 8 videos (different from the training set), and synthesized rain over these videos with varying parameters with Adobe After Effects. Each video contains around 200 to 300 frames. All subsequent results shown for each video are the average results for all frames.

Fig. 9: 8 testing rainy scenes synthesized with Adobe After Effects [53]. First row (Group a) are taken with a panning unstable camera. Second row (Group b) are from a fast moving camera (speed range between 20 to 30 km/h)

To test the algorithm performance in handling cameras with different motion, we divided the 8 testing scenes into two groups: Group a consists of scenes taken from a panning and unstable camera with slow movements; Group b from a fast moving car-mount camera (with speed range between 20 to 30 km/h). Thumbnails and the labeling of each testing scene are shown in Fig. 9.

We evaluated all the methods under the SPAC framework, i.e., SPAC-, SPAC-Avg, SPAC-RPCA, and SPAC-CNN. Five competing state-of-the-art rain removal methods were chosen for comparison. This includes two image-based method, i.e., the discriminative sparse coding (DSC) method4 [12], and the deep detail network (DDN) method5 [13]; and three video-based methods, i.e., rain removal via matrix decomposition (VMD)6 [6], via stochastic analysis (VST)7[11], and via temporal correlation/matrix low-rank completion (TCL)8 [14]. For VMD, the number of frames was set to for our test videos as advised by the author. VST assumes the background is static throughout the video sequence, and thus distorts the background severely in our test videos with fast motion. To improve its performance, we split videos into batches of 10 frames and derain each batch individually.

Rain Streak Occlusion Precision Recall Rates

Rain fall introduces edges and textures over the background. Rain removal algorithms are expected to accurately detect and remove these high frequency contents, whilst preserving the edges and textures that belong to the background. To evaluate how much of the modifications from the derain algorithms contribute to only removing the rain pixels, we calculated the rain streak edge precision-recall (PR) curves. Absolute difference values were calculated between the derain output against the scene ground truth. Different threshold values were applied to retrieve a set of binary maps, which were next compared against the ground truth rain pixel map to calculate the PR rates. Higher PR value indicates better rain streak removal quality with less scene distortion.

Average PR curves for the two groups of testing scenes by different algorithms are shown in Fig. 10. We can make a few observations.

First, SPAC-CNN shows the best performance among all methods in both Group a and Group b. The performance of SPAC-CNN compared with SPAC- and SPAC-Avg validates the efficiency of the CNN features and , which correct mis-detected rain streaks (false negative rain detection), and compensate for misalignment blur (false positive rain detection).

Second, SPAC-RPCA is competitive with SPAC- and outperforms SPAC-Avg for Group a, but falls behind these methods for Group b. RPCA models the scene background as low-rank, which gives tolerance to very small linear content shifts. It preserves background details better than SPAC-Avg when small camera motion is present. However, in Group b videos, faster camera motion and dynamic scenes reduces the similarity of scene content in Tensor ; this violates the low rank assumption required for RPCA, causing SPAC-RPCA to perform worse.

Third, for non-SPAC methods, video-based derain methods (TCL and VMD) perform better than image-based methods (DSC and DDN) for scenes in Group a. With slow camera motion, temporal correspondence can be more accurately established, which provides an advantage to video-based methods. However, with fast camera motion, temporal correspondence becomes less accurate, and the performance of video-based methods deteriorate relative to image-based methods.

Fourth, the SPAC methods are more robust to fast camera motion than other video-based rain removal methods. In particular, SPAC-RPCA is more robust than VMD and VST, although all three methods model the background as a low rank matrix. VST does not compensate for changes in the background. Hence, as seen in TABLE III, it performs poorly in our test videos. VMD relies on global frame alignment to compensate for camera motion; hence, it is robust towards slow camera motion. SPAC-RPCA aligns scene content at a superpixel granularity, which provides the best compensation for camera motion.

Fig. 10: Rain edge pixel detection precision-recall curves for different rain removal methods.

Scene Restoration PSNR/SSIM

We calculated the PSNR, and Structural Similarity (SSIM) [55] of restored scenes after rain removal between different state-of-the-art methods against the ground truth, and the results are shown in TABLE III.

Under the SPAC framework, SPAC-RPCA shows better performance than SPAC- and SPAC-Avg for Group a; however SPAC- shows better performance for Group b. For both Groups a and b, SPAC-CNN is consistently 5 dB higher than SPAC-Avg, and 2 dB higher than SPAC-. SSIM of SPAC-CNN is also consistently highest among all SPAC methods. This further validates the efficiency of the CNN detail compensation network.

Video based methods (TCL and SPAC-CNN) outperform image-based methods for Group a data (around 2dB and 3dB higher respectively than DDN, 3dB and 5dB higher respectively than DSC). For Group b, image-based methods outperform TCL and VMD. However, SPAC-CNN still holds a 3dB advantage over DDN, 4dB over DSC.

Data

Group

+

(w/o )

+

(w/o )

+

(w/o )

++

a 27.55 30.51 30.22 31.11
b 28.32 31.86 32.59 33.19
TABLE II: Derain PSNR (dB) with different features absent.
Camera Motion Clip No.   Rain DSC [12] DDN [13] VMD [6] VST [11] TCL [14] SPAC
ICCV15’ CVPR17’ CVPR17’ ICCV17’ TIP15’ RPCA Avg CNN
  Image Image Video Video Video Video Video Video Video
slow camera motion a1 28.46/0.94 25.61/0.93 28.02/0.95 26.96/0.92 26.14/0.94 29.87/0.96 27.99/0.95 29.22/0.96 24.78/0.87 29.78/0.97
a2 28.09/0.95 27.11/0.95 27.38/0.95 24.80/0.93 24.03/0.83 29.01/0.96 29.27/0.95 29.95/0.96 26.34/0.89 30.09/0.96
a3 27.84/0.93 25.08/0.92 27.41/0.94 26.45/0.90 20.50/0.70 28.82/0.95 28.01/0.94 29.15/0.95 24.72/0.85 29.75/0.96
a4 31.48/0.95 28.82/0.95 32.47/0.97 29.55/0.94 33.41/0.96 34.12/0.98 33.13/0.97 32.69/0.97 29.90/0.93 34.82/0.98
28.97/0.94 26.66/0.94 28.82/0.95 26.94/0.92 26.02/0.86 30.46/0.96 28.42/0.95 29.44/0.96 26.44/0.89 31.11/0.97
camera speed 20-30 km/h b1 28.72/0.92 28.78/0.92 29.48/0.96 24.09/0.84 22.25/0.76 28.07/0.94 29.85/0.95 29.29/0.94 26.35/0.89 31.19/0.96
b2 29.49/0.90 29.58/0.92 30.23/0.95 25.81/0.89 25.13/0.79 32.41/0.97 31.26/0.96 31.52/0.96 28.83/0.93 34.05/0.98
b3 31.04/0.95 29.55/0.95 31.39/0.97 26.12/0.90 22.08/0.84 28.29/0.94 31.50/0.96 31.71/0.97 29.55/0.94 33.73/0.98
b4 27.99/0.92 29.10/0.93 29.83/0.96 25.90/0.88 25.63/0.80 30.38/0.95 31.92/0.95 31.25/0.96 28.85/0.92 33.79/0.97
29.31/0.92 29.25/0.93 30.23/0.96 25.48/0.88 23.77/0.80 29.79/0.95 31.13/0.96 30.94/0.96 28.40/0.92 33.19/0.97
TABLE III: Rain removal performance comparison between different methods in terms of scene reconstruction PSNR/SSIM. The best performance for each video sequence have been highlighted in underlined bold letters, and the second best have been highlighted with underlines.

Ablation Studies on CNN features

We evaluated the significance of different input features to the final derain PSNR over the two groups of testing data. Three baseline CNNs with different combinations of features as input were independently trained for this evaluation. As can be seen from the results in TABLE II, a combination of all three features provides the highest PSNR. proves to be the most important feature. Visual inspection on the derain output show both + and + leave significant amount of un-removed rain. Comparing the last two columns, it can be seen that works more efficiently for Group a than b (performance improvement of 0.9 dB and 0.6 dB, respectively), which is understandable since the high frequency features are better aligned for slow cameras, which leads to more accurate detail compensation.

4.3 Rain Removal Visual Comparisons

Visual Comparisons among Spac Methods

Fig. 11 gives visual comparisons for the derain outputs from methods under the proposed SPAC framework. Several observations can be made:

(a)
Fig. 11: Visual comparison for different rain removal methods under the SPAC framework for synthetic (column 1 to 4) and real world -rain (column 5 and 6). Electronic zoom-in is advised for better detail comparison.
  1. SPAC- shows smaller background distortions as compared with SPAC-RPCA and SPAC-Avg. However, since its output relies on the correctness of the rain mask, remnants of un-detected (false negative) rain streaks can often be found; for the falsely detected rain region (false positive), background distortion (blur) can be observed. These errors have been highlighted in yellow boxes in Fig. 11.

  2. The overall quality of rain removal by SPAC-RPCA is good. The background is well preserved. There appear to be more, although less opaque, remnant rain streaks compared with SPAC-, as highlighted in green boxes. However, it gets worse when rain streaks are dense and opaque (last column).

  3. SPAC-Avg is able to clear the rain streaks much cleaner than both SPAC- and SPAC-RPCA, however it seriously blurs the background details.

  4. SPAC-CNN provides the best visual performance among all SPAC methods. The rain is removed most cleanly, and the details are restored truthfully. The enhanced details have been highlighted in red boxes for comparison with SPAC-Avg.

Visual Comparisons with Other Recent Methods

We carried out visual comparison to compare the derain performance of recent rain removal algorithms. Fig 12 shows the derain output from different competing methods for all synthetic testing data in Group a and b.

It is observed that rain can be much better removed by video-based methods. Image-based derain methods, i.e., DSC [12] and DDN [13], can only handle light and transparent rain occlusions. For those opaque rain streaks that cover a large area, they fail unavoidably. Temporal information proves to be critical in truthfully restoring the occluded details. Both VMD [6] and TCL [14] remove rain perfectly for Group a, however they create obvious blur when the camera motion is fast. The derain effect from SPAC-CNN is the most impressive for all methods.

Next, we compare these methods over real rain data. Fig 13 shows the derain results over five sequences of real world rain videos. Two adjacent frames for the last two videos are shown to demonstrate the fast camera motion. It can be seen that, although the SPAC-CNN network has been trained over synthetic rain data, it generalizes well to real world rain as well. The advantage of SPAC-CNN is very obvious under heavy rain and fast camera motion.

(a)
Fig. 12: Visual comparison between different rain removal methods on the synthetic rain testing dataset. Electronic zoom-in is advised for better detail comparison.

(a)
Fig. 13: Visual comparison between different rain removal methods on real world rain. Electronic zoom-in is advised for better detail comparison.

Derain for Veiling & Accumulated Rain

As mentioned in Sec. 3.1.3, the averaging of tensor slices is able to suppress both occluding rain streaks, and the out-of-focus rain drops near the camera (veiling rain). Fig. 14 shows two sequences of videos with veiling rain. The semi-transparent regional intensity fluctuations/distortions may not be apparent when observed in one frame; however, they are clearly visible and distracting in a video sequence. Fig. 14 shows zoom-in regions of 6 selected frames from the two videos. As can be seen, SPAC-CNN is able to suppress the fluctuations, and provide a stable clean background, even when the veiling rain covers a large portion of the frame.

(a)
Fig. 14: Suppression of veiling rain. The first and third rows are zoom-in regions of 6 selected frames from two different video sequences. The second and fourth row are the derain outputs by SPAC-CNN.

As mentioned in Sec. 1, when the scene distance is large, the rain drops appear as aerosols and accumulate over the distance similar to that of fog and haze. After rain streak removal, dehazing can be applied as an additional step to improve vision contrast. Fig. 15 shows the dehazing effect using the non-local dehazer [19] over the output of SPAC-CNN. As can be seen, the accumulated rain effect can be effectively removed. The contrast and visibility of distant objects are well restored.

(a)
Fig. 15: Restoration of accumulated rain. The first row are the original rain frames, second row are derain outputs by SPAC-CNN, and third row are the non-local [19] dehazing output applied on the results in the second row.

4.4 Execution Efficiency

We compared the average runtime between different methods for deraining a VGA resolution frame. We implemented SPAC- and SPAC-Avg in C++ with GPU acceleration. For further acceleration, global frame alignment was applied to bring the majority of scene content into alignment before accelerated template matching, where search was initially limited to a small volume, and later expanded to a larger volume if matches were not found. SPAC-RPCA and SPAC-CNN, on the other hand, were implemented in Matlab without the above acceleration methods. Results are shown in TABLE IV. As can be seen, SPAC- and SPAC-Avg are much faster than all other methods. SPAC-CNN is much faster than all non-SPAC video-based methods and comparable to the fastest image-based method DDN.

Method Platform Time Method Platform Time

DSC-ICCV15 [12]
Matlab 236.3 SPAC C++ 0.2
DDN-CVPR17 [13] Matlab 0.9 RPCA Matlab 22.3
VMD-CVPR17 [6] Matlab 119.0 Avg C++ 0.2
TCL-TIP15 [14] Matlab 169.7 CNN Matlab 3.3
TABLE IV: Execution time (in sec) comparison for different methods on deraining a single VGA resolution frame.

5 Conclusion and Discussion

5.1 Concluding Remarks

We have proposed a video-based rain removal framework based on SP content alignment and compensation. The proposed framework has been proven to be robust against fast camera motion, and can handle both opaque occluding rain streaks, and semi-transparent veiling rain that exhibits large areas of intensity fluctuations and distortions.

As the basic processing unit of our proposed algorithms, although the SP template matching can only handle translational motion, alignment errors caused by other types of motion such as rotation, scaling, and non-ridge transforms can be mitigated with global frame alignment before they are buffered (as shown in Fig. 3) [5]. Furthermore, these errors can be well modeled as sparse errors by SPAC-RPCA, and can be efficiently compensated by SPAC-CNN.

5.2 Future Work

The good performance of SPAC methods rely on the SP alignment accuracy. When camera moves even faster, SP search range needs to be enlarged accordingly, which increases computation loads significantly. We have tested scenarios with camera speed going up to 50 km/h, the PSNR becomes lower since corresponding content are less likely be found within the search range. We believe a re-trained CNN with training data from such fast moving camera will help improve the performance. We plan to further investigate rain removal for even faster moving cameras (50 km/h+) in the future.

The framework of a SP-based alignment module plus a CNN compensation module has potential for various content alignment/registration applications under severe occlusion interference. We plan to further investigate these possibilities.

Footnotes

  1. A slice is a two-dimensional section of a higher dimensional tensor, defined by fixing all but two indices [38].
  2. False positive rain pixels refer to background pixels falsely classified as rain; false negative rain pixels refer to rain pixels falsely classified as background.
  3. Testing dataset and results available at: https://github.com/hotndy/SPAC-SupplementaryMaterials
  4. Code available at: http://www.math.nus.edu.sg/~matjh/download/image_deraining/
  5. Code available at: https://xueyangfu.github.io/projects/cvpr2017.html
  6. The authors would like to thank Weihong Ren for sharing the code.
  7. Code available at: https://github.com/wwxjtu/RainRemoval_ICCV2017
  8. Code available at: http://mcl.korea.ac.kr/deraining/

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