Research Article  Open Access
Hong Deng, Dongwei Ren, Gang Xiao, David Zhang, Wangmeng Zuo, "A Coordinate Descent Method for Total Variation Minimization", Mathematical Problems in Engineering, vol. 2017, Article ID 3012910, 13 pages, 2017. https://doi.org/10.1155/2017/3012910
A Coordinate Descent Method for Total Variation Minimization
Abstract
Total variation (TV) is a wellknown image model with extensive applications in various images and vision tasks, for example, denoising, deblurring, superresolution, inpainting, and compressed sensing. In this paper, we systematically study the coordinate descent (CoD) method for solving general total variation (TV) minimization problems. Based on multidirectional gradients representation, the proposed CoD method provides a unified solution for both anisotropic and isotropic TVbased denoising (CoDenoise). With sequential sweeping and small random perturbations, CoDenoise is efficient in denoising and empirically converges to optimal solution. Moreover, CoDenoise also delivers new perspective on understanding recursive weighted median filtering. By incorporating with the Augmented Lagrangian Method (ALM), CoD was further extended to TVbased image deblurring (ALMCD). The results on denoising and deblurring validate the efficiency and effectiveness of the CoDbased methods.
1. Introduction
Total variation (TV), also known as the ROF model [1], was introduced by Rudin et al. The TV model is effective in preserving sharp and salient edges while suppressing noise and has been extensively adopted as a regularizer in various image restoration applications, for example, deblurring [2, 3], superresolution [4, 5], inpainting [6, 7], and compressed sensing [8, 9].
Recently, other image models, such as dictionarybased sparse coding [10–12] and nonlocal similarity [13–17], have been developed. Compared with these models, TV is much more efficient to be solved, making TVbased methods remain active in image and vision studies [17–24]. Moreover, TV may be complementary with the other models, and thus proper combination of them can lead to better performance [25, 26]. Besides, extensions of TV regularizer were also studied. For color images, TV can be extended to a class of vectorial TV (VTV) [27, 28], where interchannel correlation is taken into account to reduce the uneven color effects. While TV only considers firstorder gradients, Total Generalized Variation (TGV) [29] was proposed to involve higherorder derivatives. For structure extraction, relative TV [30] was employed to distinguish structure from textures. Considering that the gradient distribution of each pixel is actually spatially variant, nonlocal extension of TV model [17, 31] was presented to leverage the similar patches for adaptive distribution estimation.
A basic TV minimization problem is TVbased image denoising formulated aswhere is the TV regularizer, is the tradeoff parameter, and and are the latent clear image and the noisy observation, respectively. Various methods to solve TV denoising problem had been proposed and can be roughly categorized from three directions, that is, gradient based, Markov Random Fields (MRF) based, and CoDbased methods. First, gradient descentbased algorithms have been widely adopted in image processing tasks [18, 32–37]. As to TV minimization, gradient projection based PDE methods [1] originally were adopted to solve the associated nonlinear EulerLagrange equation. Following this line, a number of methods tried to directly solve primal variables [38–42]. To avoid nonsmoothness trap, the dual formulation of TV minimization was proposed and several variants came forward [43–45]. Recently, a hybrid primal dual scheme that alternatively solves primal and dual variables had been developed [46–48]. Most specially, Chambolle’s fixed point algorithm [43] solving dual variable is the most successful, which has been widely adopted in general image restoration methods, for example, TwIST [49], FISTA [50], and SALSA [51]. Second, TV minimization can be mapped to a class of binary MRFs [52–54], such that it can be solved by graphcut techniques. Third, another entirely different direction is to employ CoD method, decomposing optimization problem with respect to each pixel and updating coordinate variables via some appropriate patterns. For the high efficiency of decomposed scalar optimizations, the CoDbased methods are usually efficient. However, the sole attempt based on CoD to solve TV minimization [55] only considers the anisotropic TV minimization, while isotropic TV minimization is unreachable for CoDbased methods, since it cannot be decomposed with respect to each pixel.
In this paper, we systematically study the CoDbased methods for TV minimization problem. First, we provide a unified formulation of anisotropic and isotropic TV minimization problem based on multidirectional gradients representation, via which the isotropic TV regularizer can also be decomposed into a sequence of scalar convex problems with respect to each pixel. The scalar convex problem can be efficiently solved, and by sequentially updating each pixel, the CoDbased denoising (CoDenoise) algorithm converges fast. Due to the nondifferentiability of TV regularizer, CoDenoise may get stuck at nonstationary points [55–57]; however fortunately it is experimentally verified that CoDenoise can bypass nonstationary points and converge to optimal solution by adding small random perturbations. The CoDenoise algorithm only requires updating the pixels poisoned by noises, due to which the CoDenoise algorithm is more efficient than other methods, especially for low noise levels. Interestingly, the CoDenoise algorithm can be interpreted as the recursive weighted median operations on noisy images. Based on the more recent progress in weighted median filter [58, 59], the CoDenoise algorithm should be much more improved in terms of efficiency. Then, by combining variable splitting strategy and Augmented Lagrangian Method (ALM), we further embed CoDenoise algorithm to solve general image restoration problem, for example, image deblurring, resulting in the ALMCD algorithm. In deblurring problems, the blurry images are usually poisoned by relatively low level noises, and thus the incorporated CoDenoise algorithm for denoising subproblem contributes significantly to efficiency improvement of the ALMCD algorithm. Compared with TwIST, FISTA, and SALSA, ALMCD can obtain satisfactory results but is more efficient.
Our contribution can be summarized from two aspects:(i)We systematically study the CoDbased methods for TV minimization and develop an extremely simple unified CoDbased solution for both anisotropic and isotropic TV minimization. The resulting CoDenoise algorithm is more efficient than gradient based and MRF based methods and achieves satisfactory denoising results.(ii)By incorporating with ALM, CoDenoise is extended to image deblurring problem. In the deblurring problems, the blurry images usually suffer from severe blur and relatively low level noises, and thus the proposed ALMCD algorithm with CoDenoise embedded for denoising subproblem is much more efficient and can concurrently provide satisfactory deblurring quality compared with several stateoftheart methods.
This paper is organized as follows: Section 2 presents some preliminaries, including definition of TV regularizers and multidirectional gradient approximation of TV regularizers. The CoDenoise algorithm together with its convergence proof and computational complexity is proposed in Section 3. In Section 4, we embed CoDenoise to image deblurring. Section 5 demonstrates experimental results, and Section 6 ends this paper with some concluding remarks.
2. Preliminaries
In this section, we first present the definitions of the discrete anisotropic and isotropic TV operators. In previous studies, CoDbased solution is only available for anisotropic TV minimization problem. To address this, we then introduce the multidirectional gradient representation to establish the connection between the anisotropic and isotropic TV models, making it possible to use the unified CoD method for TV minimization.
2.1. The Discrete TV Operators
For an image with pixels, the discrete gradient operators including both horizontal gradient operator and vertical gradient operator are defined aswhere and . The anisotropic TV regularizer [50, 60] is defined asWith this definition, it is easy to obtain the anisotropic TV regularization with respect to coordinate asThus, the CoD method can be directly used to solve the anisotropic TV minimization problem. Similarly, the isotropic TV regularizer [50, 60] is defined asApparently the isotropic TV cannot be decomposed with respect to coordinate since the quadratic interactions with horizontal and vertical gradients, making the CoD method unfeasible to solve isotropic TV minimization problem. Therefore, to extend the results of CoD to isotropic TV minimization problem, we tempt to find a connection between and .
2.2. Multidirectional Gradients Approximation
The isotropic TV regularizer can be approximated by multidirectional gradients representation, and thus the anisotropic and isotropic TV models can be connected in a unified formulation [61]. For any pair of real numbers and , the identityalways holds, which can be discretized by Riemannian approximation. Now, let be a set of points uniformly distributed in . Equation (6) can then be discretized as
Thus we can approximate TV regularizer aswhere and and . Equation (8) provides a unified formulation of anisotropic and isotropic TV models,In later context, we will use to represent the TV regularizers.
3. The Unified Coordinate Descent Method for TVBased Denoising
With regularizer, anisotropic and isotropic TV denoising models are reformulated in the unified formwhich is exactly anisotropic TVbased denoising when and infinitely approximates isotropic TVbased denoising when increases. We thus can decompose the objective function into a sequence of onedimensional subproblems, which can be solved efficiently via simple convex optimization. With simple sequential updating pattern, we then obtain the unified CoD denoising algorithm for both anisotropic and isotropic TV minimization.
3.1. The Coordinate Subproblem
Let first present equivalent decomposition of the image denoising objective function with respect to each pixel ,where and . Vectors and are both of length , which are the coefficients of and the combinations of its 4 neighbourhoods, respectively.
3.2. Solving Subproblem
For simplicity, we unify the formulation of subproblems as
The scalar optimization problem is convex but nonsmooth. We assume (the case can be easily generalized). Let be the permutation of according to the ascending order of . Let be an ascending sequence with , , and . Let . Thus, (12) is transformed to
The solution to (13) can be obtained by making its firstorder derivative be 0,
We then discuss the solution with different cases of :(1)When , Then is the optimal solution to (13), if .(2)When , Then is the optimal solution to (13), if .(3)When , Then is the optimal solution to (13), if .(4)When ,
Since , for , we thus haveand . Then is the optimal solution to (13), if .
As a summary, we notate procedures as an operator,
Interestingly, solution (20) can be interpreted as finding the median value of vector (28), which is discussed in Section 3.4.3.
3.3. CoDenoise
Therefore, the subproblem with respect to (11) can be solved by
The following question is how to choose coordinate updating pattern. Li and Osher adopted the checkerboard pattern [55], in which the pixels are divided into black and white blocks. The pixels in the same group are not neighbors, and then the pixels in two blocks can be alternatively updated. Another greedy strategy is also popular [62], in which the selected coordinate makes the biggest contribution to the decrease of the energy function. And by the divide and conquer strategy, the corresponding coordinate can be searched with complexity [63].
The proposed CoDenoise algorithm adopted the simple cyclic updating pattern, sequentially sweeping each pixel. If the computed solution at new selected coordinate makes a big progress than that in last iteration (evaluated by a tolerance ), then it will be updated. In our implementation, we use a binary mask matrix to indicate whether a pixel will be updated or not. If any four neighbor of pixel is updated, is marked as , and the pixel will be updated in the next iteration, otherwise 0. For the nondifferentiability of TV norm, the solution generated by CoDenoise may get stuck at nonstationary points, which can be easily bypassed by adding small random perturbations. The perturbations decrease along with the increasing iteration number.
To stop the CoDenoise algorithm, we check whether the relative difference between two iterations is below tolerance ; that is,
The CoDenoise algorithm is summarized as Algorithm 1.

3.4. Convergence and Complexity
We first discuss the convergence of the CoDenoise algorithm and then analyze its computational complexity.
3.4.1. Convergence
Theorem 1. For the optimization problem equation (12), one can obtain its optimal solution using ; then holds for any .
Proof. First, observe that Suppose ; then . (1)If , we have . Therefore,(2)If , then . So for some , we have . Therefore, We hence conclude that, for any , always holds.
Theorem 2. The sequence generated by the CoDenoise algorithm converges.
Proof. From (21), we have , so . It implies that the energy over all pixels decreases, , and since it has lower bound 0, the sequence converges. From Theorem 1, we have thatconverges.
3.4.2. Computational Complexity
First, we present the analysis of computational complexity of the operator . The operation with the heaviest computational cost is to sort vectorwhich can be done by existing sorting algorithms, for example, maxheap sort, and thus the sorting the vectors in (11) can be done with computational complexity . Then, the optimal solution can be searched in at worst. And thus, the complexity of proximal operator at worst is .
Then, CoDenoise requires calling operator times in each iteration, where is the number of nonzero entries of mask matrix , proportional to the noise level, and thus the computational complexity of CoDenoise is .
3.4.3. Discussions
The proposed operator (20) can be interpreted as finding the median [55]where . Since the sequence is nondescending, we have
We discuss the equivalence of (28) and (20) using the following two cases:(1)Suppose that . The optimal solution is Now we have , so In the sequence , there are elements less than or equal to and elements greater than or equal to , and from (29), in the sequence , there are elements less than or equal to and elements greater than or equal to . And thus, is the median (28). Specially, when , all the elements in the sequence are greater than or equal to , and elements in the sequence are less than or equal to , and thus is the median (28). Also when , the same conclusion can be similarly drawn.(2)Suppose that , and it lies in . Similarly, in the sequence , there are elements less than or equal to and elements greater than or equal to , and from (29), in the sequence , there are elements less than or equal to and elements greater than or equal to . And thus, is the median (28).
With the equivalence of proposed operator (20) and finding median value (28), the CoDenoise algorithm provides an interesting interpretation of the recursive weighted median operations on noisy image. By the recently great progress of studies on median filter or reweighted filter [58, 59], the computational efficiency of the proposed CoDenoise algorithm should be further improved.
4. CoD for TVBased Image Deblurring
based image deblurring problem is formulated aswhere is convolution matrix, which is an illposed problem. By combining variable splitting and ALM, we employ ADMM to solve this problem.
First, by introducing an auxiliary variable , the based image deblurring problem is reformulated as
Then the augmented Lagrangian function of (33) iswhere is a positive penalty parameter and is related to Lagrangian vector. Then, the two variables and can be updated alternatively until some convergence criterion is satisfied. Given , the subproblem can be efficiently solved in Fourier domain,where and are Fourier and inverse Fourier transformation, respectively. Given , the subproblemcan be directly solved by the proposed CoDenoise algorithm. Finally, the parameters and are updated. The overall algorithm is summarized as Algorithm 2.

5. Experimental Results
In this section, we report the experimental results on image denoising and deblurring to validate the proposed CoDbased methods. First, as to the image denoising, CoDenoise is compared with three Chambolle’s works, that is, fixed points (CFP) algorithm on dual variables [43], firstorder primal dual (CPD) algorithm [46], and graph cut (CGC) based algorithm [53]. Then, we compare ALMCD with several stateoftheart deblurring algorithms with the denoising subproblem embedded, including accelerated IST algorithms, that is, TwIST [49] and FISTA [50], and ALMbased algorithm, that is, SALSA [51]. All the experiments ran on a 2.40 GHz Core(TM) i74700MQ processor. The CoDenoise algorithm is coded in C/C++, and ALMCD is coded in Matlab. We provide Matlab wrapper of CoDenoise which can be called by ALMCD. And for the parameter settings, the updating tolerances and are both set as . We set for anisotropic TV minimization and for isotropic TV minimization.
5.1. Image Denoising
As to the competing denoising algorithms, CFP and CPD are both only designed for isotropic TV minimization, and by modifying the projection step, CFP and CPD are easily applied to anisotropic TV minimization. Since only anisotropic TV model can be mapped to binary MRF, CGC is only feasible to anisotropic TV minimization. The denoising experiments were conducted on four 512 × 512 images, that is, Couple, Man, Hill, and Boat, shown in Figure 1, which were degraded by different Gaussian noise levels, with standard deviation (std.) as 0.05, 0.10, 0.15, and 0.20. Corresponding to each noise level, the tradeoff parameter was chosen as 0.04, 0.09, 0.16, and 0.23, respectively, with best PSNR values.
(a) Couple
(b) Man
(c) Hill
(d) Boat
For the convexity of TV minimization problem, CFD and CPD are proved to converge to global optimal solution. From the PSNR and SSIM comparison of different algorithms, shown in Tables 2 and 3, CoDenoise can also converge to the same solutions with CFD and CPD for each noise level. As to the computational efficiency shown in Table 1, for anisotropic TV denoising CoDenoise is much faster than all the competing algorithms, especially for low level noises, and for isotropic TV denoising CoDenoise has to handle more extra entries, thus being little computational expensive than CFP and comparable to CPD. Figure 2 delivers the visual denoising effect of different algorithms, and CoDenoise can achieve satisfactory denoising results.



(a) Noised image (16.48, 0.508)
(b) CPD (26.61, 0.795)
(c) CFP (26.58, 0.792)
(d) CoD (26.62, 0.795)
5.2. Image Deblurring
The proposed ALMCD algorithm is compared with TwIST, FISTA, and SALSA, where CFP is adopted to solve the involved denoising subproblem. In the experiments, the test images were degraded with Gaussian kernel with std. 7 and Gaussian noise with zero mean value and std. . The tradeoff parameter is set as for all the four algorithms.
Since blurry images usually suffer from severe blur and low level noise, the subproblem involved should be more efficiently solved by CoDenoise. Table 4 presents the deblurring results comparison for anisotropic TV deblurring, and one can see that ALMCD is significantly faster than all the competing algorithms. Even though CoDenoise is slower than CFP for isotropic TV minimization, ALMCD is instead more efficient than the competing algorithms, shown in Table 5. Particularly, SALSA adopted the same variable splitting strategy with ALMCD, generating the same subproblems, so the efficiency superiority of ALMCD over SALSA heavily confirms that CoDbased method contributes more to efficiency improvement of ALMCD. In terms of deblurring quality, both PSNR and SSIM for anisotropic and isotropic TV models, shown in Tables 4 and 5, achieved by ALMCD are comparable to all the other competing algorithms. Figure 3 presents the visual deblurring results of Boat, from which one can see that the ALMCD can obtain visually plausible deblurring results. As a summary, CoDbased methods can provide comparable solutions compared with competing algorithms, while CoDenoise for anisotropic TV model is much more efficient than all the competing denoising algorithms, and ALMCD with CoDenoise embedded to solve denoising subproblem is much faster than stateoftheart deblurring algorithms for both anisotropic and isotropic TV models.


(a) Original image
(b) TwIST (28.11, 0.907)
(c) FISTA (27.60, 0.890)
(d) Blurred image (22.15, 0.601)
(e) SALSA (28.32, 0.899)
(f) ALMCD (28.67, 0.911)
6. Conclusion
In this paper, we propose a novel unified solution based on CoD method to solve TV minimization problems. With the unified formulation, both anisotropic and isotropic TV minimization can be decomposed into scalar problems that can be efficiently solved by convex optimization. With simple cyclic updating pattern and random perturbations, CoDenoise can empirically converge to the optimal solution. Also when applied in image deblurring, the CoDenoise algorithm embedded in ALMCD makes significant contributions in terms of efficiency, compared with competing deblurring algorithms. In terms of deblurring quality, ALMCD can provide comparable or superior results, validating the effectiveness of CoDbased methods. Furthermore, with the great improvements in weighted median filter or parallel implementation, the CoDenoise algorithm should be much more efficient. Also, CoD could be extended to other TV variants, for example, nonlocal TV and vectorial TV.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
 L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D. Nonlinear Phenomena, vol. 60, no. 14, pp. 259–268, 1992. View at: Publisher Site  Google Scholar  MathSciNet
 J. Wen, J. Zhao, W. Cailing, S. Yan, and W. Wang, “Blind deblurring from single motion image based on adaptive weighted total variation algorithm,” IET Signal Processing, vol. 10, no. 6, pp. 611–618, 2016. View at: Publisher Site  Google Scholar
 D. Perrone and P. Favaro, “A Clearer Picture of Total Variation Blind Deconvolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1041–1055, 2016. View at: Publisher Site  Google Scholar
 K. Dillon, Y. Fainman, and Y.P. Wang, “Computational estimation of resolution in reconstruction techniques utilizing sparsity, total variation, and nonnegativity,” Journal of Electronic Imaging, vol. 25, no. 5, Article ID 053016, 2016. View at: Publisher Site  Google Scholar
 M. K. Ng, H. Shen, E. Y. Lam, and L. Zhang, “A total variation regularization based superresolution reconstruction algorithm for digital video,” Eurasip Journal on Advances in Signal Processing, vol. 2007, Article ID 74585, 2007. View at: Publisher Site  Google Scholar
 Q. Cheng, H. Shen, L. Zhang, and P. Li, “Inpainting for remotely sensed images with a multichannel nonlocal total variation model,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 175–187, 2014. View at: Publisher Site  Google Scholar
 T. F. Chan, J. Shen, and H.M. Zhou, “Total variation wavelet inpainting,” Journal of Mathematical Imaging and Vision, vol. 25, no. 1, pp. 107–125, 2006. View at: Publisher Site  Google Scholar  MathSciNet
 V. M. Patel, R. Maleh, A. C. Gilbert, and R. Chellappa, “Gradientbased image recovery methods from incomplete Fourier measurements,” IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 94–105, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 J. Yang, Y. Zhang, and W. Yin, “A fast alternating direction method for TVL1L2 signal reconstruction from partial Fourier data,” IEEE Journal on Selected Topics in Signal Processing, vol. 4, no. 2, pp. 288–297, 2010. View at: Publisher Site  Google Scholar
 M. Elad, B. Matalon, and M. Zibulevsky, “Image denoising with shrinkage and redundant representations,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1924–1931, June 2006. View at: Publisher Site  Google Scholar
 M. Aharon, M. Elad, and A. Bruckstein, “KSVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at: Publisher Site  Google Scholar
 J. Mairal, F. Bach, and J. Ponce, “Taskdriven dictionary learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 791–804, 2012. View at: Publisher Site  Google Scholar
 J. Xu, L. Zhang, W. Zuo, D. Zhang, and X. Feng, “Patch group based nonlocal selfsimilarity prior learning for image denoising,” in Proceedings of the 15th IEEE International Conference on Computer Vision, ICCV 2015, pp. 244–252, December 2015. View at: Publisher Site  Google Scholar
 W. Yang, J. Liu, S. Yang, and Z. Quo, “Image superresolution via nonlocal similarity and group structured sparse representation,” in Proceedings of the Visual Communications and Image Processing, VCIP 2015, December 2015. View at: Publisher Site  Google Scholar
 R. Xiong, H. Liu, X. Zhang et al., “Image denoising via bandwise adaptive modeling and regularization exploiting nonlocal similarity,” IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5793–5805, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 S. Wang, L. Jiao, and S. Yang, “SAR Images Change Detection Based on Spatial Coding and Nonlocal Similarity Pooling,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 8, pp. 3452–3466, 2016. View at: Publisher Site  Google Scholar
 H. Liu, R. Xiong, S. Ma, X. Fan, and W. Gao, “Nonlocal extension of total variation regularization for image restoration,” in Proceedings of the 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014, pp. 1102–1105, June 2014. View at: Publisher Site  Google Scholar
 D. Perrone and P. Favaro, “Total variation blind deconvolution: the devil is in the details,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 2909–2916, IEEE, June 2014. View at: Publisher Site  Google Scholar
 D. Needell and R. Ward, “Stable image reconstruction using total variation minimization,” SIAM Journal on Imaging Sciences, vol. 6, no. 2, pp. 1035–1058, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 R. I. Boţ and C. Hendrich, “Convergence analysis for a primaldual monotone + skew splitting algorithm with applications to total variation minimization,” Journal of Mathematical Imaging and Vision, vol. 49, no. 3, pp. 551–568, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 L. Ambrosio and S. Di Marino, “Equivalent definitions of $\text{BV}$ space and of total variation on metric measure spaces,” Journal of Functional Analysis, vol. 266, no. 7, pp. 4150–4188, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 H. Wu, Y. Wu, and Z. Wen, “Texture Smoothing Based on Adaptive Total Variation,” Advances in Intelligent Systems and Computing, vol. 277, pp. 43–54, 2014. View at: Publisher Site  Google Scholar
 R. W. Liu, L. Shi, W. Huang, J. Xu, S. C. H. Yu, and D. Wang, “Generalized total variationbased MRI Rician denoising model with spatially adaptive regularization parameters,” Magnetic Resonance Imaging, vol. 32, no. 6, pp. 702–720, 2014. View at: Publisher Site  Google Scholar
 J. Xu, D. Ren, L. Zhang, and D. Zhang, “Patch Group Based Bayesian Learning for Blind Image Denoising,” in Computer Vision – ACCV 2016 Workshops, vol. 10116 of Lecture Notes in Computer Science, pp. 79–95, Springer International Publishing, Berlin, Germany, 2017. View at: Publisher Site  Google Scholar
 J.L. Starck, M. Elad, and D. L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1570–1582, 2005. View at: Publisher Site  Google Scholar  MathSciNet
 T. Zeng, Study on the variational models and dictionary learning [Ph.D. thesis], Université ParisNordParis XIII, France, 2007.
 S. Ono and I. Yamada, “Decorrelated vectorial total variation,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 4090–4097, June 2014. View at: Publisher Site  Google Scholar
 J.F. Aujol and S. H. Kang, “Color image decomposition and restoration,” Journal of Visual Communication and Image Representation, vol. 17, no. 4, pp. 916–928, 2006. View at: Publisher Site  Google Scholar
 K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM Journal on Imaging Sciences, vol. 3, no. 3, pp. 492–526, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 L. Xu, Q. Yan, Y. Xia, and J. Jia, “Structure extraction from texture via relative total variation,” ACM Transactions on Graphics, vol. 31, no. 6, article 139, 2012. View at: Publisher Site  Google Scholar
 H. Xu, Q. Sun, N. Luo, G. Cao, and D. Xia, “Iterative nonlocal totalvariation regularization method for image restoration,” PLoS ONE, vol. 8, no. 6, Article ID e65865, 2013. View at: Publisher Site  Google Scholar
 L. Antonelli, V. De Simone, and D. di Serafino, “On the application of the spectral projected gradient method in image segmentation,” Journal of Mathematical Imaging and Vision, vol. 54, no. 1, pp. 106–116, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 R. De Asmundis, D. di Serafino, and G. Landi, “On the regularizing behavior of the SDA and SDC gradient methods in the solution of linear illposed problems,” Journal of Computational and Applied Mathematics, vol. 302, pp. 81–93, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 S. Bonettini, R. Zanella, and L. Zanni, “A scaled gradient projection method for constrained image deblurring,” Inverse Problems, vol. 25, no. 1, Article ID 015002, 015002, 23 pages, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 R. Fletcher, “A limited memory steepest descent method,” Mathematical Programming, vol. 135, no. 12, Ser. A, pp. 413–436, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 C. C. Gonzaga and R. M. Schneider, “On the steepest descent algorithm for quadratic functions,” Computational Optimization and Applications. An International Journal, vol. 63, no. 2, pp. 523–542, 2016. View at: Publisher Site  Google Scholar  MathSciNet
 R. De Asmundis, D. di Serafino, W. W. Hager, G. Toraldo, and H. Zhang, “An efficient gradient method using the Yuan steplength,” Computational Optimization and Applications. An International Journal, vol. 59, no. 3, pp. 541–563, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Li and F. Santosa, “A computational algorithm for minimizing total variation in image restoration,” IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 987–995, 1996. View at: Publisher Site  Google Scholar
 T. F. Chan, H. M. Zhou, and R. H. Chan, “Continuation method for total variation denoising problems,” in Proceedings of the Advanced Signal Processing Algorithms, pp. 314–325, San Diego, CA, USA, 1995. View at: Publisher Site  Google Scholar
 C. R. Vogel and M. E. Oman, “Fast, robust total variationbased reconstruction of noisy, blurred images,” IEEE Transactions on Image Processing, vol. 7, no. 6, pp. 813–824, 1998. View at: Publisher Site  Google Scholar  MathSciNet
 C. R. Vogel and M. E. Oman, “Iterative methods for total variation denoising,” SIAM Journal on Scientific Computing, vol. 17, no. 1, pp. 227–238, 1996. View at: Publisher Site  Google Scholar  MathSciNet
 D. Yi, “An iterative scheme for total variationbased image denoising,” Journal of Scientific Computing, vol. 58, no. 3, pp. 648–671, 2014. View at: Publisher Site  Google Scholar  MathSciNet
 A. Chambolle, “An algorithm for total variation minimization and applications,” Journal of Mathematical Imaging and Vision, vol. 20, no. 12, pp. 89–97, 2004. View at: Publisher Site  Google Scholar  MathSciNet
 D. Yi, B. Choi, and E.Y. Kim, “An effective method for solving nonlinear equations and its application,” Applied Mathematics and Computation, vol. 220, pp. 568–579, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 T. F. Chan, K. Chen, and J. L. Carter, “Iterative methods for solving the dual formulation arising from image restoration,” Electronic Transactions on Numerical Analysis, vol. 26, pp. 299–311, 2007. View at: Google Scholar  MathSciNet
 A. Chambolle and T. Pock, “A firstorder primaldual algorithm for convex problems with applications to imaging,” Journal of Mathematical Imaging and Vision, vol. 40, no. 1, pp. 120–145, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 T. F. Chan, G. H. Golub, and P. Mulet, “A nonlinear primaldual method for total variationbased image restoration,” SIAM Journal on Scientific Computing, vol. 20, no. 6, pp. 1964–1977, 1999. View at: Publisher Site  Google Scholar  MathSciNet
 M. Zhu and T. Chan, “An efficient primaldual hybrid gradient algorithm for total variation image restoration,” UCLA CAM Report, pp. 8–34, 2008. View at: Google Scholar
 J. M. BioucasDias and M. A. Figueiredo, “A new TwIST: twostep iterative shrinkage/thresholding algorithms for image restoration,” IEEE Transactions on Image Processing, vol. 16, no. 12, pp. 2992–3004, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 A. Beck and M. Teboulle, “Fast gradientbased algorithms for constrained total variation image denoising and deblurring problems,” IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2419–2434, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 M. V. Afonso, J. M. BioucasDias, and M. A. Figueiredo, “Fast image recovery using variable splitting and constrained optimization,” IEEE Transactions on Image Processing, vol. 19, no. 9, pp. 2345–2356, 2010. View at: Publisher Site  Google Scholar  MathSciNet
 J. Darbon and M. Sigelle, “A Fast and Exact Algorithm for Total Variation Minimization,” in Pattern Recognition and Image Analysis, vol. 3522 of Lecture Notes in Computer Science, pp. 351–359, Springer Berlin Heidelberg, Berlin, Heidelberg, 2005. View at: Publisher Site  Google Scholar
 A. Chambolle, “Total variation minimization and a class of binary MRF models,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3757, pp. 136–152, 2005. View at: Publisher Site  Google Scholar
 D. Goldfarb and W. Yin, “Parametric maximum flow algorithms for fast total variation minimization,” SIAM Journal on Scientific Computing, vol. 31, no. 5, pp. 3712–3743, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Li and S. Osher, “A new median formula with applications to PDE based denoising,” Communications in Mathematical Sciences, vol. 7, no. 3, pp. 741–753, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, “Pathwise coordinate optimization,” The Annals of Applied Statistics, vol. 1, no. 2, pp. 302–332, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 T. F. Chan and K. Chen, “An optimizationbased multilevel algorithm for total variation image denoising,” Multiscale Modeling and Simulation, vol. 5, no. 2, pp. 615–645, 2006. View at: Publisher Site  Google Scholar
 Q. Zhang, L. Xu, and J. Jia, “100+ times faster weighted median filter (WMF),” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, pp. 2830–2837, June 2014. View at: Publisher Site  Google Scholar
 S. Perreault and P. Hébert, “Median filtering in constant time,” IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2389–2394, 2007. View at: Publisher Site  Google Scholar  MathSciNet
 W. Zuo and Z. Lin, “A generalized accelerated proximal gradient approach for totalvariationbased image restoration,” IEEE Transactions on Image Processing, vol. 20, no. 10, pp. 2748–2759, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 O. V. Michailovich, “An iterative shrinkage approach to totalvariation image restoration,” IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1281–1299, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 Y. Li and S. Osher, “Coordinate descent optimization for l^{1} minimization with application to compressed sensing; a greedy algorithm,” Inverse Problems and Imaging, vol. 3, no. 3, pp. 487–503, 2009. View at: Publisher Site  Google Scholar
 Y. Nesterov, “Efficiency of coordinate descent methods on hugescale optimization problems,” SIAM Journal on Optimization, vol. 22, no. 2, pp. 341–362, 2012. View at: Publisher Site  Google Scholar  MathSciNet
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Copyright © 2017 Hong Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.