Image Fusion With Cosparse Analysis Operator
The paper addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited robustness in fusion processing. In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms state-of-the-art fusion methods.
Fusion of multi-focus images is a popular approach for generating an all-in-focus image with less artifacts and higher quality [1, 2]. It relies on the idea of combining a captured sequence of multi-focus images with different focal settings. It plays a crucial role in many fundamental fields such as machine vision, remote sensing and medical imaging [3, 4, 5]. During the last decade, two types of fusion approaches have been developed: the transform domain-based approaches and the spatial frequency-based approaches. Most existing transform domain-based methods [6, 7, 8, 9] work with a limited basis, and make fusion excessively dependent on the choice of a basis. The latter approaches [10, 11, 12, 13] require highly accurate sub-pixel or sub-region estimates, and thus, fail to perform well in elimination of undesirable artifacts.
A prevalent approach for image fusion is based on synthesis sparse model. Various manifold fusion methods have been proposed to explore this model [3, 16, 17, 18, 14, 15]. The core ideas here are to describe source images as linear combination of a few columns from a prespecified dictionary, merge sparse coefficients by a fusion function, and then generate an all-in-focus image using reconstructed sparse coefficients. While there has been extensive research on synthesis sparse model, the analysis sparse model [19, 20, 21, 22] is a recent construction that stands as a powerful alternative. This new model represents a signal by multiplying it with the so-called analysis operator , and it emphasizes zero elements of the resulting analyzed vector which describes the subspace containing the signal . It promotes strong linear dependencies between rows of , leads to much richer union-of-subspaces, and shows the promise to be superior in various applications [23, 24, 25].
In this letter, we develop a novel fusion algorithm based on an analysis sparse model which allows for simultaneous restoration and fusion of multi-focus images. Specifically, we formulate the fusion problem as a regularized inverse-problem of estimating an all-in-focus image given its reconstructed form, and take advantages of the correlations among multiple captured images for fusion using a cosparsity prior. The corresponding algorithm exploits a combination of variable splitting and alternating direction method of multipliers (ADMM) to learn the analysis operator , that promotes cosparsity. Furthermore, a fusion function generates the cosparse representation of an all-in-focus image. Advantages of this approach are: more flexible cosparse representation compared to the synthesis sparse approaches and better image restoration and fusion performance. The proposed approach also widens the applicability of analysis sparse model.
Ii Problem Formulation
Let be a sequence of multi-focus images of the same scene acquired with different focal parameters. Our goal is to recover an all-in-focus image from captured images . The model for describing the relationship between the sequence of multi-focus images and an unknown all-in-focus image can be formally expressed as
where is a blurring operator that denotes the physical process of capturing -th multi-focus image [26, 27], and is an additive zero-mean white Gaussian noise matrix, with entries drawn at random from the normal distribution . The blurring operator in most cases is unknown and irreversible, therefore it is too complex to find a sequence out of all possible operators. Instead, it is more favorable to seek a compromise between the physical modelling of image capture and signal approximation.
Assuming the cosparsity prior, each image patch of the image is said to have a cosparse representation over known analysis operator with , if there exits a sparse analyzed vector . The model emphasizes on zero coefficients of and defines as a sub-matrix of with rows that belong to the co-support set . The co-support set consists of the row indices, which determine the subspace that is orthogonal to. Then, can be characterized by its co-support. Every image patch is estimated by solving the following optimization problem
where is a denoised estimation of , is a tolerance error, and and are respectively the -norm and -norm of a vector. Since is unknown, we choose to use the cosparse representation vector for recovering through optimal fusion of cosparse coefficients . Using correlations among multiple images, the proposed approach defines fusion function as that generates an optimal cosparse representation and returns the corresponding indices of image patches. Thus, a natural generalization of the problem (2) for recovering a clean all-in-focus image patch is given as
The role of in (3) is to provide a meaningful constraint on how closely the optimal patch approximates . We replace the cosparse representation by the corresponding optimal of the input image , with respect to the cosparse analysis operator . Therefore, the major sub-problems here are learning the analysis operator and the fusion function .
Iii Robust Fusion via Analysis Sparse Model
Analysis operator learning aims at constructing an operator suitable for a family of signals of interest. We first propose a practical approach for learning the analysis operator by variable splitting and ADMM. Then, with the analysis operator fixed, we define the optimal fusion function.
Iii-a Analysis Operator Learning
Suppose a training set is formed from a set of clean vectorized images contaminated by an additive zero-mean white Gaussian noise . Our task is to find which enforces the coefficient vector to be sparse for each . This problem for can be cast as
where is a regularization parameter. To prevent from being degenerate, it is common to constrain its rows so that they have their -norms equal to one. Then, the constraint set can be described as
where is the index set of the rows in corresponding to zero elements in , denotes the rank of a matrix, and elements of are zeros.
The problem (4)-(5) is non-convex with respect to variables . A fundamental approach to addressing it is to alternate between the two sets of variables and , i.e., minimizing over one while keeping the other fixed. Motivated by the first-order surrogate (FOS) approach  and ADMM [29, 30], we propose the FOS-ADMM algorithm for cosparse coding.
With fixed, we update each column of . For notation simplicity, we drop the column index in and . We observe that the objective function (4) for fixed , i.e., is the sum of two functions: and where . It enables us to transform the problem of minimizing into the following constraint optimization problem
where is the augmented Lagrangian penalty and is the vector of Lagrange multipliers at -th iteration. Note that the updates for and are separated into (7a) and (7b). The sub-problem (7a) is a convex quadratic problem, and it can be easily solved by the FOS approach  that consists of solving iteratively the optimization problem
where +, is the gradient of , is the Lipschitz constant of , and stands for the transpose.
where is the sign function and stands for the component-wise product.
With fixed, we turn to updating that amounts to obtaining each row of . The update of should be affected only by those columns of that are orthogonal to it . Denote as indices of those columns, then the corresponding optimization problem can be written as
where , form the sub-matrices of and which containing columns found to be orthogonal to , respectively.
Iii-B Local Optimal Fusion
When the analysis operator is learned, we yet can not directly compute the cosparse representation of . Instead, we work with the collection of the cosparse representations , and then seek the optimal one to recover the corresponding all-in-focus patch.
Using the sliding window technique, each image can be divided into small patches, from left-top to right-bottom. For convenience, we introduce a matrix to extract the -th block from the image . Visible artifacts may occur on block boundaries, and we also introduce overlapping patch of length for each small patch, and demand that the reconstructed all-in-focus patches would agree to each other on the overlapping areas. According to , the block, or equivalently, the set of indices corresponding to the biggest value in the set is chosen to reconstruct the fused image. Thus, the problem of finding the optimal cosparse representation can be formulated as
Given the optimal fusion function (12), the fusion problem can be cast as the basis pursuit problem with the cosparse regularization term . Thus, the problem (3) can be replaced by the following problem of finding the initial estimate of the fused patch
Iii-C Global Reconstruction
The above explained local optimal fusion is used to recover local details for each all-in-focus patch, respecting spatial compatibility between neighbouring patches. In order to remove possible artifacts and improve spatial smoothness, the global reconstruction constraint between the initial image estimate , formed from all ’s, and the final estimate can be applied to make a further improvement.
The size of is suitable to represent a small image patch, and it is too small to apply for the entire image. Therefore, we expand the size of and define
as the global analysis operator where and are indices of the boundaries. Using the result from the local optimal fusion, the entire image can be redefined using the reconstruction constraint by solving the problem
where is the parameter controlling the sparsity penalty and representation fidelity. Hence, the entire process of the optimal fusion is summarized in Algorithm 1.
Iv Experimental Results
We verify the restoration and fusion performance of the proposed approach by visual comparisons, and then discuss the quantitative assessments. We have tested our approach for a number of images, and here one representative example is shown. Specifically, fusion experiments over the standard multi-focus dataset  are conducted. Throughout all the experiments, the tolerance error in the proposed approach is set as , the maximum number of iterations is , the patch size is , and the overlapping length is . During the analysis operator learning, the generated training set consists of two-dimensional normalized samples of size extracted at random from the natural images. Considering the tradeoff between fusion quality and computations, the analysis operator size is fixed to . All the experiments are performed on a PC running Inter(R) Xeon(R) 3.40GHz CPU.
In the noise-free () and noisy () cases, the proposed approach is compared with well-known fusion approaches, including the image fusion approach based on spatial frequency in discrete cosine transform (SF-DCT)  and the sparse representation K-SVD-based image fusion approach (SR-KSVD) . The fusion results of the noise-free images “Dog” are shown in Fig. 1, including the magnified details in the lower right corners of the images. There are noticeable differences in the edge of the wall. The SF-DCT method (see Fig. 1(c)) produces blocking artifacts, and the SR-KSVD method (see Fig. 1(d)) introduces undesired smoothing. Our proposed method (see Fig. 1(e)) eliminates some artificial distortions, and gives better visual result. To test the robustness of our approach, we add Gaussian noise to the multi-focus images. In Fig. 2, the results for the approach tested are shown for . Note that the SF-DCT method needs the denosing preprocessing, and then fuses multi-focus images. Fig. 2(c) shows circle blurring effect around strong boundaries. The image (see Fig. 2(d)) also shows the blurring effect for the SR-KSVD method. Our approach is capable of providing restoration and fusion simultaneously, and it performs the best as it visually appears in Fig. 2(e).
More objectively, we test the impact of different parameters selection on the proposed approach. The objective evaluation is based on the following two state-of-the-art fusion performance metrics: , which measures how well the mutual information from the source images is preserved in the fused image; and , which evaluates how well the edge information transfers from the source images to the fused image. The values of and range from to , with representing the ideal fusion. First, we conduct several experiments for different patch sizes, and compare the performance in the noise-free () and noisy () cases for the aforementioned methods in Fig. 3. The employed patch sizes are . In either case , the values of (see Fig. 3(a)) and (see Fig. 3(b)) for the proposed approach are always larger than for the SF-DCT and SR-KSVD methods. It means that our approach preserves well the mutual information and transfers efficiently the edge information from source images. When patch size is , the values of and are optimal. Thus, we set the patch size , and also conduct fusion experiments with different noise levels . The results are shown in Fig. 4. It can be seen that all the methods tested show larger values when is equal to zero. With the increase of the noise level, the values of and gradually decrease, while the proposed method performs the best. Table I presents the average running time of the aforementioned methods. As expected, the proposed approach achieves the restoration and fusion with high-quality in reasonable time.
|Measure||Methods in noise-free case||Methods in noisy case ()|
A novel fusion approach for combining multi-focus noisy images into a higher quality all-in-focus image based on analysis sparse model has been presented. Using the cosparsity prior assumption, we have proposed an analysis operator learning approach based on ADMM. Furthermore, an efficient fusion processing via the learned analysis operator has been presented. Extensive experiments have demonstrated that the proposed approach can fuse images with remarkably high-quality, and have confirmed the highly competitive performance of our proposed algorithm. As a future work, a more flexible penalty function can be employed in the fusion problem, which can possibly lead to even better results.
-  A. A. Goshtasby, and S. Nikolov, “Image fusion: advances in the state of the art,” Inf. Fusion, vol. 8, no. 2, pp. 114–118, Apr. 2007.
-  T. Wan, C. Zhu, and Z. Qin, “Multifocus image fusion based on robust principal component analysis,” Pattern Recognit. lett., vol. 34, no. 9, pp. 1001–1008, Jul. 2013.
-  Q. Zhang, and M. D. Levine, “Robust multi-Focus image fusion using multi-task sparse representation and spatial context,” IEEE Trans. Image Process., vol. 25, no. 5, pp. 2045–2058, May. 2016.
-  V. N. Gangapure, S. Banerjee, and A. S. Chowdhury, “Steerable local frequency based multispectral multifocus image fusion,” Inf. Fusion, vol. 23, pp. 99–115, May. 2015.
-  L. Cao, L. Jin, H. Tao, G. Li, Z. Zhuang, and Y. Zhang, “Multi-focus image fusion based on spatial frequency in discrete cosine transform domain,” IEEE Signal Process. Lett., vol. 22, no. 2, pp. 220–224, Feb. 2015.
-  P. Burt and E. Adelson, “The laplacian pyramid as a compact image code,” IEEE Trans. Commun., vol. 31, no. 4, pp. 532–540, Apr. 1983.
-  V. Aslantas, and R. Kurban, “Fusion of multi-focus images using differential evolution algorithm,” Expert Syst. Appl., vol. 37, no. 12, pp. 8861–8870, Dec. 2010.
-  S. Li, and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Inf. Fusion, vol. 26, no. 7, pp. 971–979, Jul. 2008.
-  Z. Zhou, S. Li, and B. Wang, “Multi-scale weighted gradient-based fusion for multi-focus images,” Image Vision Comput., vol. 20, pp. 60–72, Nov. 2014.
-  J. Tian, and L. Chen, “Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure,” Signal Process., vol. 92, no. 9, pp. 2137–2146, Sep. 2012.
-  A. L. Da Cunha, J. Zhou, and M. N. Do, “The nonsubsampled contourlet transform: theory, design, and applications,” IEEE Trans. Image Process., vol. 15, no. 10, pp. 3089–3101, Oct. 2006.
-  O. Rockinger, “Image sequence fusion using a shift-invariant wavelet transform,” in Proc. IEEE Int. Image Process., Santa Barbara, CA, 1997, vol. 3, pp. 288–291.
-  Q. Zhang, and B. Guo, “Multifocus image fusion using the nonsubsampled contourlet transform,” Signal Process., vol. 89, no. 7, pp. 1334–1346, Jul. 2009.
-  S. Ambat, S. Chatterjee, and K. Hari, “Fusion of algorithms for compressed sensing,” IEEE Trans. Signal Process., vol. 61, no. 14, pp. 3699–3704, May. 2010.
-  H. Li, L. Li, and J. Zhang, “Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering,” Opt. Commun., vol. 342, pp. 1–11, May. 2015.
-  B. Yang, and S. Li, “Multifocus image fusion and restoration with sparse representation,” IEEE Trans. Instrum. Meas., vol. 59, no. 4, pp. 884–892, Apr. 2010.
-  M. Nejati, S. Samavi, and S. hirani, “Multi-focus image fusion using dictionary-based sparse representation,” Inf. Fusion, vol. 25, pp. 72–84, Sep. 2015.
-  R. Gao, S. A. Vorobyov, and H. Zhao, “Multi-focus image fusion via coupled dictionary training,” in Proc. IEEE 41st Int. Conf. Acoustics, Speech and Signal Process., Shanghai, China, 2016, pp. 1666–1670.
-  M. Elad, P. Milanfar, and R. Rubinstein, “Analysis versus synthesis in signal priors,” Inv. Probl., vol. 23, no. 3, pp. 947–968, Jun. 2007.
-  J. Dong, W. Wang, W. Dai, M. D. Plumbley, Z. Han, and J. Chambers, “Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning,” IEEE Trans. Signal Process., vol. 64, no. 2, pp. 417–431, Jan. 2016.
-  M. Seibert, J. Wörmann, R. Gribonval, and M. Kleinsteuber, “Learning co-Sparse analysis operators with separable structures,” IEEE Trans. Signal Process., vol. 64, no. 1, pp. 120–130, Jan. 2016.
-  S. Nam, M. E. Davies, M. Elad, and R. Gribonval, “The cosparse analysis model and algorithms,” Appl. Comput. Harmon. Anal., vol. 34, no. 1, pp. 30–56, Jan. 2013.
-  R. Rubinstein, T. Peleg, and M. Elad, “Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model,” IEEE Trans. Signal Process., vol. 61, no. 3, pp. 661–677, Feb. 2013.
-  M. Yaghoobi, S. Nam, R. Gribonval, and M. E. Davies, “Constrained overcomplete analysis operator learning for cosparse signal modelling,” IEEE Trans. Signal Process., vol. 61, no. 9, pp. 2341–2355, May. 2013.
-  S. Hawe, M. Kleinsteuber, and K. Diepold, “Analysis operator learning and its application to image reconstruction,” IEEE Trans. Image Process., vol. 22, no. 6, pp. 2138–2150, Feb. 2013.
-  S. Pertuz, D. Puig, M. A. Garcia, and A. Fusiello, “Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images,” in IEEE Trans. Image Process., vol. 22, no. 3, pp. 1242–1251, Mar. 2013.
-  M. Subbarao, T. Choi, and A. Nikzad, “Focusing techniques,” Opt. Eng., vol. 32, pp. 2824–2836, Mar. 1993.
-  J. Mairal, “Incremental majorization-minimization optimization with application to large-scale machine learning,” SIAM J. Optim., vol. 25, no. 2, pp. 829–855, Apr. 2015.
-  J. Eckstein, and D. Bertsekas, “On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators,” Math. Program., vol. 55, no. 3, pp. 293–318, Nov. 1992.
-  S. Xie, and S. Rahardja, “Alternating direction method for balanced image restoration,” IEEE Trans. Image Process., vol. 21, no. 11, pp. 4557–4567, Nov. 2012.
-  M. Nejati, S. Samavi, and S. Shirani, “Multi-focus image fusion using dictionary-based sparse representation,” Inf. Fusion, vol. 25, pp. 72–84, Sep. 2015.
-  M. Hossny, S. Nahavandi, and D. Creighton, “Comments on information measure for performance of image fusion,” Electron. Lett., vol. 44, no. 18, pp. 1066–1067, Aug. 2008.
-  C. Xydeas, and V. Petrović, “Objective image fusion performance measure,” Electron. Lett., vol. 36, no. 4, pp. 308–309, Feb. 2000.