Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration

Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration

Abstract

Low-rank signal modeling has been widely leveraged to capture non-local correlation in image processing applications. We propose a new method that employs low-rank tensor factor analysis for tensors generated by grouped image patches. The low-rank tensors are fed into the alternative direction multiplier method (ADMM) to further improve image reconstruction. The motivating application is compressive sensing (CS), and a deep convolutional architecture is adopted to approximate the expensive matrix inversion in CS applications. An iterative algorithm based on this low-rank tensor factorization strategy, called NLR-TFA, is presented in detail. Experimental results on noiseless and noisy CS measurements demonstrate the superiority of the proposed approach, especially at low CS sampling rates. 1 2

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1 Introduction

Inspired by the nonlocal self-similarity of image patches [3], various image processing algorithms have been proposed to investigate the low-rank property of image patch groups [9, 11, 17, 31, 44]. In general, these methods first select a reference patch and then search for similar patches across the image to form a group. Following this, the patches in this group are vectorized and stacked to a matrix. Since these patches are similar, the constructed matrix has a low-rank property. Via performing this low-rank model on every (overlapping) patch in the image, state-of-the-art image restoration results have been achieved.

One common issue in the above algorithms is that the original two-dimensional (2D) patches are vectorized to construct the group matrix, which loses the spatial structure within the image patch. We propose a tensor based algorithm to retain this structure while still leveraging the advantages of low-rank patch models. Tensor factorization methods [19] offer a useful way to learn latent structure from complex multiway data, and have been used in image processing tasks [43]. These methods decompose the tensor data into a set of factor matrices (one for each mode or “way” of the tensor), that can be used as a latent feature representation for the objects in each of the tensor modes [33]. Recently tensor approaches have been applied in computer vision, such as image denoising [32], and video denoising [42]. We consider the general image restoration problem with specific applications to compressive sensing [12] using the tensor factor analysis (TFA) approach. In particular, instead of vectorizing the image patches, we impose low-rank TFA to the 3D image patch groups.

Figure 1: An illustration of the NLR-TFA model. The left image is the initial estimate using some fast algorithm. Each tensor is concatenated by a reference image patch and its similar patches. Blue cubes represent full-rank tensors; green cubes represent low-rank tensors. The right image (PSNR=dB) is reconstructed using NLR-TFA from compressive sensing (CS) measurements at a CS rate of only . Notice how the proposed method can restore rich semantic content and fine structure of the image, even when the sensing rate is extremely low.

To be concrete, the image restoration problem aims to estimate the latent clean image given the observation (the corrupted/compressive measurement) and the measurement matrix , which can be formulated as

(1)

where denotes the measurement noise and is usually modeled by . The problem has different variants for different : when is the identity matrix, this is a denoising problem [16]; when is a diagonal matrix whose diagonal elements are either 1 or 0, keeping or removing corresponding pixels, this becomes image inpainting [63]; and when and , this is a compressive sensing (CS) problem.

We focus on the CS problem [14, 28], and consider the image reconstruction with a very limited number of compressive measurements [50, 51, 52, 58]. A framework of the proposed NLR-TFA method is shown in Fig. 1. There has been recent interest in using deep learning technique for CS problems [30]. A reconstruction network is developed in [20, 58], and an ADMM-net is proposed in [47]; while the former paper focused on block-wise CS, the latter one only considered CS-MRI [28]. By contrast, we develop a tensor-based framework for general CS applications. Further, to overcome the large-scale matrix inversion in CS, we further propose to pre-train a deep convolutional neural network to approximate the expensive matrix inversion operator [41].

The remainder of this paper is organized as follows. Section 2 presents background knowledge on tensors and CS. Section 3 derives our algorithm. Extensive results are provided in Section 4 to demonstrate the superiority of the proposed algorithm relative to other leading approaches, and Section 5 concludes the paper.

2 Background

2.1 Tensor Decomposition

Tensors (of order higher than two) are arrays indexed by three or more indices. Specifically, matrices are two-way arrays, and tensors are three- or higher-way arrays. In the sequel, we mainly focus on way tensors and everything naturally generalizes to higher-order tensors.

A way tensor is an outer product of unit vectors , , and :

(2)

where is the vector outer product and is a scalar.

The of a tensor is the minimum number of tensors that sum to . Therefore, a way tensor can be written as

(3)

where unit vectors , , and are tensor factors and is a scalar that evaluates the significance of tensor factors . For brevity, can be denoted as , where , , , and . When the rank is low, the number of elements needed to represent is dramatically decreased after tensor decomposition, \ie, from to .

Practically, most tensor decomposition problems are NP-hard. However, in most real applications, as long as the tensor does not have too many components, and the components are not adversarially chosen, tensor decomposition can be computed in polynomial time [34]. The tensor-decomposition problem seeks to estimate , , and coefficients from a tensor . Adopting a least-squares fitness criterion, the problem is

(4)

In this work, we employ Jenrich’s algorithm [21] to solve this problem. In Algorithm 1, “” denotes pseudo-inverse of a matrix.

0:  Tensor .
1:  Pick two random unit vectors .
2:  Compute ;
3:  Compute ;
4:  ’s are eigenvectors of , ’s are eigenvectors of ;
5:  Given and , we can get ’s and ’s by solving a linear system followed by normalization;
6:  Output: Tensor factors ’s, ’s, ’s and coefficients ’s
Algorithm 1 Jenrich’s Algorithm

2.2 Compressive Sensing

Compressive sensing is a signal acquisition technique that enables sampling a signal at sub-Nyquist rates [12]. In CS, a reconstruction algorithm is used to recover the original high-dimensional signal from a small number of random linear measurements. Taking compressive measurements can be viewed as a process of linearly mapping an -dimensional signal vector to an -dimensional measurement vector , , using a measurement matrix , \ie, . Since the matrix is rank-deficient, there exists more than one that yields the same measurement . In this paper, the compressive sensing rate (CSr) is defined as CSr.

To recover , one searches for the vector that possesses a certain structure among all the vectors that satisfy . In the case of a sparse , a popular method is to solve the optimization problem

(5)

This problem is convex and known formally as basis pursuit denoising (BPDN). It has been shown in [6][13] that if is sufficiently sparse and satisfies certain properties, then the -sparse signal can be accurately recovered from random linear measurements [5]. Equation (5) can be equivalently translated to the following unconstrained optimization problem

(6)

where is a regularization parameter. Various methods can be used to solve the above minimization problem [49, 59, 57], and in this work we adopt the alternative direction multiplier method (ADMM) framework [2, 23]. Specifically, we consider the application of image CS [14, 51, 52], and beyond sparsity, we propose a new tensor factorization approach to exploit the high-order structure in the image patches, seeking high reconstruction performance at extremely low CSr, \eg, CSr<0.05. Refer to Fig. 3 for one example of reconstructed image using our proposed algorithm, compared with other leading algorithms at CSr (with image size ).

3 Method

We propose a new model that recovers compressively sensed images using low-rank tensor factor analysis and ADMM. First, we generate a tensor from the estimated image based on patch grouping. Then we impose low rankness on the tensor after tensor decomposition. This new low-rank tensor is fed into a global objective function which is solved by ADMM. These two steps are iteratively performed until satisfying some criterion. The complete algorithm is shown in Algorithm 2.

3.1 Patch-Based Low-Rank Tensor Factorization

Given the observation , we first obtain the estimated image using some fast algorithm, \eg, the DCT or wavelet based algorithm [18, 59]. In denoising, can be simply set equal to . Then the estimated image is divided into overlapping patches . The basic assumption underlying the proposed approach is that a sufficient number of similar patches can be found for any reference patch, a.k.a, the nonlocal self-similarity (NSS) prior [3]. For each reference patch , we perform a -nearest-neighbor search for the nonlocal similar patches across the image to form a group , where is the number of similar patches (including the reference patch itself). Here, the Euclidean distance of pixel intensity is used as the metric to group patches. By concatenating the grouped patches on the third dimension in ascending order of Euclidean distances, we generate a tensor for reference patch

(7)

As has zeros distance to itself, it is always found as the leading patch in , \ie, . Eventually we have tensors and each tensor corresponds to a reference patch. The coordinates of the grouped patches are also recorded for later image aggregation. Suppose the size of each patch is , then the size of generated tensor is . It has been shown that the grouped patches can be denoised by low-rank approximation [4]. In this work, the low-rankness is imposed on the tensor by taking the most significant tensor factors after tensor decomposition

(8)

where selects the most significant tensor factors of . Therefore, has rank . The significance of tensor factors is evaluated by ’s in (3).

Figure 2: Aggregating low-rank tensors to images. The original image is on top left. The PSNR of images reconstructed from - to - tensors are dB, dB, dB, dB, dB, dB, dB, and dB respectively.

Under the assumption that the grouped patches have similar structure, has a low-rank property, which ensures that can be represented by a relatively low-rank tensor . This low-rank imposition shares the same spirit with the hard thresholding algorithm [1]. In Fig. 2, we impose low rankness on tensors with size generated from a clean image using the above approach, and then aggregate the tensors back to images. As can be seen, reconstructed images with low-rank tensors can accurately approximate the original image. The images aggregated from - to - tensors are highly similar (with PSNR merely increased by dB), which indicates that in this case - tensors are adequate to represent the original image.

3.2 Image Recovery via ADMM

With the reconstructed low-rank tensors , the following optimization problem is proposed for CS recovery:

(9)

where denotes the tensor formed for each reference patch and is a regularization parameter. The closed-form solution for this quadratic optimization problem is

(10)

where is the Hermitian transpose operation, denotes the results of averaging all of the similar patches for each reference patch, and each entry of corresponds to an image pixel location whose value is the number of overlapping patches that cover this pixel location. In (10), the matrix to be inverted can be large, for which conjugate gradient descent (CG) is usually applied [8]. We adopt ADMM to solve this problem, introducing auxiliary variable . Applying ADMM to (9), we obtain the global objective function

(11)

where is the Lagrange multiplier, and is the penalty parameter. Instead of minimizing and simultaneously, ADMM decomposes the problem into two subproblems that minimizes w.r.t and , respectively. More specifically, the optimization problem in (3.2) consists of the following iterations:

(12)
(13)
(14)

Both and admit closed-form solutions. For fixed and ,

(15)

Then we use the updated to update ,

(16)

where .

0:  The observation measurement . Initialization:
1:  Set parameters , , , , , , , and .
2:  Pretrain for inverting matrix using (18);
3:  Obtain an estimated image from observation using a fast CS method;
4:  Divide into a set of overlapping patches;
5:  Initialize ; Image Restoration:
6:  for  do
7:     Divide into a set of overlapping patches with size ;
8:     Form a set of tensors with size using patch block matching;
9:     Decompose tensors using Jenrich’s Algorithm;
10:     Impose low rankness on tensors via (8) and generate a set of - tensors; ADMM:
11:     Initialize ,
12:     for  do
13:        Update via (15);
14:        if  is a partial Fourier transform matrix then
15:           Update via (21);
16:        else
17:           Update via (19);
18:        end if
19:        Update via (14);
20:     end for
21:     Update
22:  end for
23:  Output: The reconstructed image .
Algorithm 2 CS via Low-Rank TFA

Pretrained Deep Convolutional Architectures

In (16), the term involves an expensive matrix inversion, which makes direct computation of impractical. An inversion-free approach proposed by [41] addresses this problem by learning a convolutional neural network to approximate the matrix inversion. Note that training this neural network is data-independent since is only dependent on . Applying the Sherman-Morrison-Woodbury formula, we reduce this matrix inversion to a smaller scale,

(17)

where has the dimension . To approximate , a trainable deep convolutional neural network parameterized by is employed, \ie, . is learned by minimizing the sum of two reconstruction losses of two auto-encoders with shared weights

(18)

where can be computed directly, and is sampled from publicly available image datasets [10]. Note that is pretrained for different and . By plugging the learned into (17), we obtain a reusable term as the replacement for the cumbersome inversion matrix. Hence is updated by

(19)

Fourier space solution

Equation (16) can be solved by transforming the problem from the image space into the Fourier space when is a partial Fourier transform matrix [11]. For a down-sampling matrix and a Fourier transform matrix , is substituted into (16)

(20)

where the inverse matrix is a diagonal matrix and thus can be computed easily. Equation (20) is equivalent to

(21)

Therefore, can be obtained by applying inverse Fourier transform to terms in the brackets of the right hand side of (21).

4 Experiments

Image Method CSr
0.02 0.04 0.06 0.08 0.10
Barbara BM3D-CS 16.44, 0.389 18.03, 0.422 19.70, 0.470 21.85, 0.561 23.70, 0.635
TVAL3 21.32, 0.588 22.58, 0.620 23.28, 0.639 23.78, 0.654 24.25, 0.667
NLR-CS 16.79, 0.400 18.90, 0.433 20.56, 0.501 24.00, 0.659 25.13, 0.725
D-AMP 15.08, 0.354 17.76, 0.417 20.44, 0.496 22.66, 0.623 24.05, 0.660
Ours 26.15, 0.812 27.29, 0.835 27.40, 0.837 28.05, 0.848 28.30, 0.852
Boats BM3D-CS 16.76, 0.398 17.98, 0.440 20.11, 0.507 23.21, 0.624 24.65, 0.687
TVAL3 21.58, 0.634 23.66, 0.681 24.57, 0.699 25.22, 0.713 25.75, 0.725
NLR-CS 17.20, 0.409 19.41, 0.489 21.76, 0.559 23.76, 0.638 25.52, 0.711
D-AMP 15.52, 0.365 18.41, 0.456 20.76, 0.528 22.85, 0.600 25.22, 0.703
Ours 30.79, 0.898 31.73, 0.909 31.84, 0.910 32.35, 0.916 32.33, 0.912
Cameraman BM3D-CS 16.84, 0.432 18.34, 0.496 20.62, 0.585 22.32, 0.654 23.85, 0.711
TVAL3 20.33, 0.557 21.53, 0.579 22.02, 0.580 22.37, 0.578 23.19, 0.688
NLR-CS 17.00, 0.436 19.10, 0.531 21.60, 0.620 23.99, 0.709 26.25, 0.789
D-AMP 16.13, 0.401 17.51, 0.461 19.84, 0.562 21.74, 0.629 23.61, 0.705
Ours 25.88, 0.828 26.47, 0.829 26.66, 0.823 27.16, 0.824 27.64, 0.819
Foreman BM3D-CS 18.96, 0.610 21.06, 0.680 25.48, 0.726 29.80, 0.831 31.69, 0.866
TVAL3 23.35, 0.777 26.81, 0.808 28.47, 0.821 29.45, 0.828 30.02, 0.833
NLR-CS 19.64, 0.645 22.33, 0.695 28.09, 0.809 31.68, 0.865 34.15, 0.900
D-AMP 19.42, 0.639 22.07, 0.687 26.31, 0.750 29.12, 0.813 31.90, 0.872
Ours 34.01, 0.934 34.61, 0.936 35.18, 0.937 35.33, 0.939 36.02, 0.939
House BM3D-CS 18.12, 0.518 20.35, 0.590 25.79, 0.755 29.78, 0.816 30.50, 0.824
TVAL3 22.57, 0.706 24.58, 0.724 26.36, 0.740 27.63, 0.766 28.68, 0.814
NLR-CS 18.80, 0.554 21.77, 0.620 27.16, 0.761 31.23, 0.835 33.66, 0.869
D-AMP 17.74, 0.492 19.93, 0.577 24.00, 0.693 27.39, 0.758 29.84, 0.818
Ours 30.42, 0.873 31.76, 0.884 33.07, 0.898 34.32, 0.910 34.80, 0.913
Lena BM3D-CS 16.09, 0.427 18.22, 0.511 20.47, 0.603 23.55, 0.694 24.63, 0.729
TVAL3 21.60, 0.671 23.23, 0.693 23.88, 0.697 24.41, 0.701 24.81, 0.705
NLR-CS 16.71, 0.461 19.07, 0.545 21.36, 0.649 25.47, 0.746 25.82, 0.771
D-AMP 15.53, 0.399 17.21, 0.485 19.93, 0.567 22.29, 0.681 24.60, 0.733
Ours 28.51, 0.881 29.70, 0.894 29.85, 0.889 30.42, 0.898 30.65, 0.896
Monarch BM3D-CS 14.55, 0.352 15.24, 0.390 17.65, 0.500 19.75, 0.581 21.64, 0.666
TVAL3 17.31, 0.537 18.91, 0.577 19.82, 0.595 20.56, 0.611 21.24, 0.624
NLR-CS 14.69, 0.357 16.03, 0.413 18.53, 0.552 21.84, 0.686 25.12, 0.796
D-AMP 14.01, 0.334 14.86, 0.366 17.55, 0.497 19.84, 0.587 21.77, 0.675
Ours 26.14, 0.884 27.28, 0.895 28.21, 0.901 28.97, 0.904 29.25, 0.904
Parrots BM3D-CS 16.62, 0.506 19.32, 0.613 23.07 0.693 25.04, 0.761 26.10, 0.787
TVAL3 21.11, 0.714 22.90, 0.722 23.47, 0.714 24.38, 0.770 25.84, 0.804
NLR-CS 17.39, 0.550 20.84, 0.681 24.95, 0.782 28.18, 0.835 30.35, 0.875
D-AMP 16.89, 0.520 19.55, 0.619 22.62, 0.684 24.10, 0.711 26.31, 0.792
Ours 28.28, 0.899 28.88, 0.893 29.22, 0.890 29.55, 0.887 29.84, 0.885
Average BM3D-CS 16.80, 0.454 18.57, 0.518 21.61, 0.605 24.42, 0.690 25.85, 0.738
TVAL3 21.15, 0.648 23.02, 0.676 23.98, 0.685 24.72, 0.703 25.47, 0.732
NLR-CS 17.28, 0.477 19.68, 0.551 23.00, 0.654 26.27, 0.747 28.25, 0.805
D-AMP 16.29, 0.438 18.41, 0.509 21.43, 0.610 23.75, 0.675 25.91, 0.745
Ours 28.77, 0.876 29.71, 0.884 30.18, 0.886 30.77, 0.891 31.10, 0.890
Table 1: Reconstruction PSNR (dB) and SSIM of images using different CS recovery methods at different CSr.

We conduct experiments on both noisy and noiseless CS mearurements of test images: Barbara, Boats, Cameraman, Foreman, House, Lena, Monarch, and Parrots. All images are resized to . Since excellent results have been obtained when the CS rate is large, \ie [11, 29], we here focus on testing cases with limited number of measurements (). The CS measurements are generated by pseudo-radial sampling of the test images in the Fourier domain. Unlike traditional random sampling schemes, pseudo-radial sampling produces streaking artifacts which are more difficult to remove. We also perform experiments using an random sensing matrix generated by a standard Gaussian distribution. The deep convolutional architecture is pretrained by the sensing matrix then used in ADMM to solve the expensive matrix inversion. A DCT based CS recovery algorithm [52] is used for the initial image estimate. The main parameters of the proposed method are set as follows: patch size , number of similar patches for each reference patch , and the imposed low rank is . The regularization parameter and the ADMM parameter are tuned separately for each sensing rate. In practice, we have found that ADMM converges fast after a few iterations and the performance gain is mainly from our low-rank TFA. Thus we set the number of outer-loop iterations to and the number of inner loop iterations . Experimental results for noiseless CS measurements and noisy CS measurements are evaluated by peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [40].

(a)
(b)
(c)
(d)
Figure 3: Reconstruction results from noiseless CS measurements for Foreman at CSr. (a) Original image; (b) TVAL3 (23.35 dB); (c) NLR-CS (19.64 dB); (d) Ours (34.01 dB).
(a)
(b)
(c)
(d)
Figure 4: Reconstruction results from noiseless CS measurements for Monarch at CSr. (a) Original image; (b) TVAL3 (19.82 dB); (c) NLR-CS (18.51 dB); (d) Ours (28.21 dB).

4.1 Baselines

We compare our results with four competitive CS image restoration algorithms: BM3D-CS [15], TVAL3 [22], NLR-CS [11] and D-AMP [29]. BM3D-CS is a nonparametric method that applies block matching and 3D filtering algorithm [9] on compressive sensing. TVAL3 restores images by combining the classic augmented Lagrangian multiplier method with total-variation regularization. NLR-CS is currently a state-of-the-art method that uses a nonlocal low-rank regularization method along with ADMM to solve image CS problems. D-AMP employs a denoiser in an approximate message passing framework, achieving state-of-the-art performance on noisy measurements, especially at high CS rates. The source codes of these baseline methods are downloaded from the respective author’s website and parameters for these algorithms are set to their default values.

4.2 Experiments with Noiseless CS Measurements

We first perform CS image restoration experiments from noiseless measurements using pseudo-radial sampling scheme at five different CS rates, \ie, CSr = {, , , , }. Table 1 summarizes the results of our proposed algorithm compared with various CS inversion algorithms at different CSr values. Both BM3D-CS and the state-of-the-art methods NLR-CS and D-AMP suffer at extremely low CSr. As CSr increases, NLR-CS yields significant performance improvements, which leads one of its results at higher CSr () to surpass other methods. Our proposed method achieves the best performance in all cases when CSr. On average, our proposed NLR-TFA algorithm outperforms all other competing methods at CSr = {, , , , and }. The PSNR gains of our NLR-TFA over BM3D-CS, TVAL3, NLR-CS and D-AMP can be as much as dB, dB, dB, and dB, respectively. Furthermore, it can be observed that our average reconstruction PSNR only decreases by dB as CSr decreases from to , while this number for NLR-CS and D-AMP are dB and dB, respectively, indicating that the proposed method is very stable at low CSr, \ie, with limited number of measurements.

To evaluate the reconstruction visually, two examples of restored Foreman and Monarch images at CSr of and are shown in Figs. 3 and 4. It is evident that our method recovers the best visual quality among all competiting methods. Large-scale sharp edges and small-scale fine structures are both reconstructed in two images. In particular, at extremely low CSr of , our NLR-TFA method (Fig. 3(d)) can effectively approximate the original image while some other methods, such as NLR-CS, can only reconstruct scratches (Fig. 3(c)).

Image Method CSr
0.02 0.04 0.06 0.08
Barbara D-AMP 15.79 17.56 19.20 21.96
Ours 24.13 25.52 26.38 27.22
Boats D-AMP 16.06 17.93 19.84 22.47
Ours 26.04 27.44 28.59 29.51
Cameraman D-AMP 16.66 18.32 19.79 22.42
Ours 23.59 24.66 25.41 26.15
Foreman D-AMP 20.50 22.78 25.75 28.40
Ours 27.72 29.23 30.66 31.47
House D-AMP 18.61 20.85 24.42 26.97
Ours 26.68 28.20 29.48 30.17
Lena D-AMP 16.34 18.15 19.77 22.15
Ours 24.63 26.04 26.99 27.78
Monarch D-AMP 14.50 15.68 17.94 19.93
Ours 21.57 23.01 24.19 24.89
Parrots D-AMP 17.49 19.38 22.56 25.24
Ours 25.92 27.19 27.96 28.54
Average D-AMP 16.99 18.83 21.15 23.69
Ours 25.04 26.41 27.46 28.22
Table 2: PSNR(dB) of reconstructions from measurements generated by random Gaussian sampling of 8 images at different CSr.

We next generate CS measurements by sampling the test images with a random Gaussian sensing matrix. A deep convolutional neural network is pretrained using the sensing matrix to solve the large-scale matrix inversion problem in ADMM. We compare our results with D-AMP which provides random Gaussian sensing implementations in their sourcing code. The reconstruction results at CSr of , , and are shown in Table 2. Our proposed method outperforms D-AMP on all test images and sensing rates. In addition, the runtime of the algorithm using this inversion-free approach is also much faster than using approaches with inner-loop updates, such as conjugate gradient (CG). This demonstrates the efficiency and accuracy of the deep convolutional architecture on approximating the inversion matrix.

(a)
(b)
(c)
(d)
(e)
Figure 5: Reconstruction results of Boats images from noisy CS measurements with noise standard deviation equal to at CSr. (a) Original image; (b) TVAL3 (24.02 dB); (c) NLR-CS (23.48 dB); (d) DAMP (24.71 dB); (e) Ours (30.97 dB).
(a)
(b)
Figure 6: Comparison of different CS methods with noisy measurements for Lena and Parrots images. (a) CSr. (b) CSr=.
Patch Size CSr
0.02 0.04 0.06 0.08 0.10
34.01 34.61 35.18 35.33 36.02
31.16 31.95 32.72 32.88 33.59
29.63 30.41 31.37 31.68 32.24
28.57 29.45 30.44 30.86 31.33
27.39 28.61 29.60 30.11 30.54
Table 3: PSNR(dB) of reconstructions with variant patch sizes at different CSr for foreman image.

Patch Size Selection We have conducted experiments using different patch sizes. As can be seen from Table 3 (in the pseudo-radial sampling scheme), smaller patch sizes lead to better reconstruction results. When we adopt patch size of on the foreman image, the PSNR is significantly increased compared to results with larger patch sizes. Similar conclusions are found on other images as well. In addition, the running time of our model is largely influenced by the tensor size. For tensor size of (image patch size and similar patches for each reference patch), the average running time at CSr= is about 10 minutes on a desktop with i7 CPU @3.4GHz and 24G RAM.

4.3 Experiments with Noisy CS Measurements

Similar experiments are conducted with noisy CS measurements to show the robustness of our algorithm to noise. The noisy CS measurements are obtained by adding random zero-mean Gaussian noise to the noiseless CS measurements. We test the algorithms at three levels of noise, with standard deviation ={, , }. The PSNR comparison of all methods for Boats and Monarch images at sensing rates of and , respectively, are shown in Fig. 6. Our proposed NLR-TFA outperforms other competing methods at all levels of noise. Furthermore, D-AMP shows great robustness to noise while BM3D-CS and NLR-CS are relatively sensitive to high-level noise. As the noise level increases, the reconstruction performance of our algorithm decreases slowly. This shows that the proposed method is robust to noise. In Fig. 5, we show the reconstructions of Boats images using various algorithms at CSr= and . The proposed NLR-TFA clearly yields the best reconstruction and is robust to noise.

5 Conclusion

We have presented a low-rank tensor-factor-analysis-based approach to solve image-restoration problems. A tensor is generated by concatenating similar patches from the estimated image for each exemplar patch. Low-rank is imposed on the tensors to exploit non-local correlation and the high-order structure information via tensor factorization. ADMM is employed on low-rank tensors, where we use either a pretrained convolutional architecture or Fourier approaches to solve a matrix inversion. Experimental results demonstrate that the proposed NLR-TFA method outperforms state-of-the-art algorithms on CS image reconstruction at low CS sampling rates.

We have demonstrated the superiority of our model in image CS with three-way tensors, which can also be used in depth CS [25, 26, 53], polarization CS  [39] and dynamic range CS [48]. Future work includes extending the model to four-way tensors for video CS [24, 27, 35, 36, 45, 46, 54, 55, 56, 60, 61], hyperspectral image CS [7, 62] and to five-way tensors for joint spectral-temporal CS [37, 38].

Footnotes

  1. This work was performed when Xinyuan Zhang was a summer intern at Nokia Bell Labs in 2017.
  2. Corresponding author.

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