An adaptive total variational despeckling model based on gray level indicator frame

Previous Article
Stable recovery of a non-compactly supported coefficient of a Schrödinger equation on an infinite waveguide
IPI Home
This Issue
Next Article
Image retinex based on the nonconvex TV-type regularization

doi: 10.3934/ipi.2020068

An adaptive total variational despeckling model based on gray level indicator frame

Yu Zhang ^1, , Songsong Li ^2, , Zhichang Guo ^1,, and Boying Wu ^1,

1.	School of Mathematics, Harbin Institute of Technology, Harbin 150001, China
2.	School of Management, Harbin Institute of Technology, Harbin 150001, China

^* Corresponding author: Zhichang Guo

Received October 2019 Revised July 2020 Published November 2020

Fund Project: This work is partially supported by the National Natural Science Foundation of China (11971131, U1637208, 61873071, 51476047, 11871133, 71773024), the Natural Sciences Foundation of Heilongjiang Province of China (LC2018001, G2018006) and the Heilongjiang Postdoctoral Scientific Research Developmental Fund (LBH-Q18064)

For the characteristics of the degraded images with multiplicative noise, the gray level indicators for constructing adaptive total variation are proposed. Based on the new regularization term, we propose the new convex adaptive variational model. Then, considering the existence, uniqueness and comparison principle of the minimizer of the functional. The finite difference method with rescaling technique and the primal-dual method with adaptive step size are used to solve the minimization problem. The paper ends with a report on numerical tests for the denoising of images subject to multiplicative noise, the comparison with other methods is provided as well.

Keywords: Image denoising, multiplicative noise, adaptive total variation, finite difference, primal-dual algorithm.

Mathematics Subject Classification: Primary:68U10, 68Q25;Secondary:32A70.

Citation: Yu Zhang, Songsong Li, Zhichang Guo, Boying Wu. An adaptive total variational despeckling model based on gray level indicator frame. Inverse Problems & Imaging, doi: 10.3934/ipi.2020068

References:

[1]	G. Aubert and J.-F. Aujol, A variational approach to remove multiplicative noise, SIAM Journal on Applied Mathematics, 68 (2008), 925-946. doi: 10.1137/060671814. Google Scholar
[2]	A. Beck and M. Teboulle, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, IEEE Transactions on Image Processing, 18 (2009), 2419-2434. doi: 10.1109/TIP.2009.2028250. Google Scholar
[3]	A. C. Bovik, T. S. Huang and D. C. Munson, A generalization of median filtering using linear combinations of order statistics, IEEE Trans Acoustics Speech Signal Processing, 31 (1983), 1342-1350. Google Scholar
[4]	A. C. Bovik, T. S. Huang and D. C. Munson, The effect of median filtering on edge estimation and detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (1987), 181-194. doi: 10.1109/TPAMI.1987.4767894. Google Scholar
[5]	K. Bredies, K. Kunisch and T. Pock, Total generalized variation, SIAM Journal on Imaging Sciences, 3 (2010), 492-526. doi: 10.1137/090769521. Google Scholar
[6]	A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, 20 (2004), 89-97. Google Scholar
[7]	A. Chambolle, An algorithm for mean curvature motion, Interfaces and Free Boundaries, 6 (2004), 195-218. doi: 10.4171/IFB/97. Google Scholar
[8]	A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145. doi: 10.1007/s10851-010-0251-1. Google Scholar
[9]	C. Chaux, J.-C. Pesquet and N. Pustelnik, Nested iterative algorithms for convex constrained image recovery problems, SIAM Journal on Imaging Sciences, 2 (2009), 730-762. doi: 10.1137/080727749. Google Scholar
[10]	Y. Chen and M. Rao, Minimization problems and associated flows related to weighted p energy and total variation, SIAM Journal on mathematical Analysis, 34 (2003), 1084-1104. doi: 10.1137/S0036141002404577. Google Scholar
[11]	Y. Chen and T. Wunderli, Adaptive total variation for image restoration in BV space, Journal of Mathematical Analysis and Applications, 272 (2002), 117-137. doi: 10.1016/S0022-247X(02)00141-5. Google Scholar
[12]	P. L. Combettes and J.-C. Pesquet, A douglas-rachford splitting approach to nonsmooth convex variational signal recovery, IEEE Journal of Selected Topics in Signal Processing, 1 (2007), 564-574. doi: 10.1109/JSTSP.2007.910264. Google Scholar
[13]	A. Dauwe, B. Goossens, H. Luong and W. Philips, A fast non-local image denoising algorithm, Electronic Imaging, 6812 (2008), 681210. doi: 10.1117/12.765505. Google Scholar
[14]	C.-A. Deledalle, L. Denis, S. Tabti and F. Tupin, MuLoG, or how to apply Gaussian denoisers to multi-channel SAR speckle reduction?, IEEE Transactions on Image Processing, 26 (2017), 4389-4303. doi: 10.1109/TIP.2017.2713946. Google Scholar
[15]	G. Dong, Z. Guo and B. Wu, A convex adaptive total variation model based on the gray level indicator for multiplicative noise removal, Abstract and Applied Analysis, 2013 (2013), 1-21. doi: 10.1155/2013/912373. Google Scholar
[16]	Y. Dong and T. Zeng, A convex variational model for restoring blurred images with multiplicative noise, SIAM Journal on Imaging Sciences, 6 (2013), 1598-1625. doi: 10.1137/120870621. Google Scholar
[17]	S. Durand, J. Fadili and M. Nikolova, Multiplicative noise removal using L1 fidelity on frame coefficients, Journal of Mathematical Imaging and Vision, 36 (2010), 201-226. doi: 10.1007/s10851-009-0180-z. Google Scholar
[18]	E. Esser, X. Zhang and T. F. Chan, A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science, Siam Journal on Imaging Sciences, 3 (2010), 1015-1046. doi: 10.1137/09076934X. Google Scholar
[19]	K. Florian, B. Kristian, P. Thomas and S. Rudolf, Second order total generalized variation (TGV) for MRI, Magnetic Resonance in Medicine, 65 (2011), 480-491. doi: 10.1002/mrm.22595. Google Scholar
[20]	E. Giusti, Minimal surfaces and functions of bounded variation, Monographs in Mathematics, Birkhäuser Verlag, Basel, 80 1984, 7-26. doi: 10.1007/978-1-4684-9486-0. Google Scholar
[21]	T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343. doi: 10.1137/080725891. Google Scholar
[22]	H. Na, M. Kang, M. Jung and M. Kang, Nonconvex TGV regularization model for multiplicative noise removal with spatially varying parameters, Inverse Problems and Imaging, 13 (2019), 117-147. doi: 10.3934/ipi.2019007. Google Scholar
[23]	P. Kornprobst, R. Deriche and G. Aubert, Image sequence analysis via partial differential equations, Journal of Mathematical Imaging and Vision, 11 (1999), 5-26. doi: 10.1023/A:1008318126505. Google Scholar
[24]	K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 16 (2007), 2080-2095. doi: 10.1109/TIP.2007.901238. Google Scholar
[25]	T. Le, R. Chartrand and T. J. Asaki, A variational approach to reconstructing images corrupted by poisson noise, Journal of Mathematical Imaging and Vision, 27 (2007), 257-263. doi: 10.1007/s10851-007-0652-y. Google Scholar
[26]	S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage, IEEE Transactions on Geoscience and Remote Sensing, 50 (2012), 606-616. doi: 10.1109/TGRS.2011.2161586. Google Scholar
[27]	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639. doi: 10.1109/34.56205. Google Scholar
[28]	T. Pock, D. Cremers, H. Bischof and A. Chambolle, An algorithm for minimizing the mumford-Shah functional, IEEE International Conference on Computer Vision, (2009), 1133-1140. doi: 10.1109/ICCV.2009.5459348. Google Scholar
[29]	L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F. Google Scholar
[30]	C. Poynton, Digital Video and HD: Algorithms and Interfaces, 2$^nd$ edition, Elsevier, USA, 2012. doi: 10.1016/C2010-0-68987-5. Google Scholar
[31]	X. Shan, J. Sun and Z. Guo, Multiplicative noise removal based on the smooth diffusion equation, Journal of Mathematical Imaging and Vision, 61 (2019), 763-779. doi: 10.1007/s10851-018-00870-z. Google Scholar
[32]	R. Soorajkumar, P. K. Kumar, D. Girish and J. Rajan, Fourth order PDE based ultrasound despeckling using ENI classification, IEEE International Conference on Signal Processing and Communications (SPCOM), (2016). doi: 10.1109/SPCOM.2016.7746633. Google Scholar
[33]	D. M. Strong and T. F. Chan, Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing, Journal of Mathematical Imaging and Vision, (1996). Google Scholar
[34]	M. Tur, K. C. Chin and J. W. Goodman, When is speckle noise multiplicative?, Applied Optics, 21 (1982), 1157-1159. doi: 10.1364/AO.21.001157. Google Scholar
[35]	L. Verdoliva, R. Gaetano, G. Ruello and G. Poggi, Optical-driven nonlocal SAR despeckling, IEEE Geoscience and Remote Sensing Letters, 12 (2015), 314-318. doi: 10.1109/LGRS.2014.2337515. Google Scholar
[36]	Z. Wang, A. C. Bovik and H. R. Sheikh, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 53 (2015), 2765-2774. Google Scholar
[37]	J. Weickert, Anisotropic Diffusion in Image Processing, Teubner Stuttgart, ECMI Series, Teubner, Stuttgart, 1998. Google Scholar
[38]	C. Wu and X.-C. Tai, Augmented Lagrangian method, dual Methods, and split Bregman iteration for ROF, vectorial TV, and high order models, SIAM Journal on Imaging Sciences, 3 (2010), 300-339. doi: 10.1137/090767558. Google Scholar
[39]	J.-H. Yang, X.-L. Zhao, T.-H. Ma, Y. Chen, T.-Z. Huang and M. Ding, Emote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization, Journal of Computational and Applied Mathematics, 363 (2020), 124-144. doi: 10.1016/j.cam.2019.06.004. Google Scholar
[40]	X.-L. Zhao, F. Wang and M. K. Ng, A new convex optimization model for multiplicative noise and blur removal, SIAM Journal on Imagingences, 7 (2014), 456-475. doi: 10.1137/13092472X. Google Scholar
[41]	Y. Zhao, J. G. Liu, B. Zhang, W. Hong and Y.-R. Wu, Adaptive total variation regularization based SAR image despeckling and despeckling evaluation index, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 2765-2774. doi: 10.1109/TGRS.2014.2364525. Google Scholar
[42]	Z. Zhou, Z. Guo, G. Dong, J. Sun, D. Zhang and B. Wu, A doubly degenerate diffusion model based on the gray level indicator for multiplicative noise removal, IEEE Transactions on Image Processing, 24 (2015), 249-260. doi: 10.1109/TIP.2014.2376185. Google Scholar

show all references

References:

[1]	G. Aubert and J.-F. Aujol, A variational approach to remove multiplicative noise, SIAM Journal on Applied Mathematics, 68 (2008), 925-946. doi: 10.1137/060671814. Google Scholar
[2]	A. Beck and M. Teboulle, Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems, IEEE Transactions on Image Processing, 18 (2009), 2419-2434. doi: 10.1109/TIP.2009.2028250. Google Scholar
[3]	A. C. Bovik, T. S. Huang and D. C. Munson, A generalization of median filtering using linear combinations of order statistics, IEEE Trans Acoustics Speech Signal Processing, 31 (1983), 1342-1350. Google Scholar
[4]	A. C. Bovik, T. S. Huang and D. C. Munson, The effect of median filtering on edge estimation and detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9 (1987), 181-194. doi: 10.1109/TPAMI.1987.4767894. Google Scholar
[5]	K. Bredies, K. Kunisch and T. Pock, Total generalized variation, SIAM Journal on Imaging Sciences, 3 (2010), 492-526. doi: 10.1137/090769521. Google Scholar
[6]	A. Chambolle, An algorithm for total variation minimization and applications, Journal of Mathematical Imaging and Vision, 20 (2004), 89-97. Google Scholar
[7]	A. Chambolle, An algorithm for mean curvature motion, Interfaces and Free Boundaries, 6 (2004), 195-218. doi: 10.4171/IFB/97. Google Scholar
[8]	A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145. doi: 10.1007/s10851-010-0251-1. Google Scholar
[9]	C. Chaux, J.-C. Pesquet and N. Pustelnik, Nested iterative algorithms for convex constrained image recovery problems, SIAM Journal on Imaging Sciences, 2 (2009), 730-762. doi: 10.1137/080727749. Google Scholar
[10]	Y. Chen and M. Rao, Minimization problems and associated flows related to weighted p energy and total variation, SIAM Journal on mathematical Analysis, 34 (2003), 1084-1104. doi: 10.1137/S0036141002404577. Google Scholar
[11]	Y. Chen and T. Wunderli, Adaptive total variation for image restoration in BV space, Journal of Mathematical Analysis and Applications, 272 (2002), 117-137. doi: 10.1016/S0022-247X(02)00141-5. Google Scholar
[12]	P. L. Combettes and J.-C. Pesquet, A douglas-rachford splitting approach to nonsmooth convex variational signal recovery, IEEE Journal of Selected Topics in Signal Processing, 1 (2007), 564-574. doi: 10.1109/JSTSP.2007.910264. Google Scholar
[13]	A. Dauwe, B. Goossens, H. Luong and W. Philips, A fast non-local image denoising algorithm, Electronic Imaging, 6812 (2008), 681210. doi: 10.1117/12.765505. Google Scholar
[14]	C.-A. Deledalle, L. Denis, S. Tabti and F. Tupin, MuLoG, or how to apply Gaussian denoisers to multi-channel SAR speckle reduction?, IEEE Transactions on Image Processing, 26 (2017), 4389-4303. doi: 10.1109/TIP.2017.2713946. Google Scholar
[15]	G. Dong, Z. Guo and B. Wu, A convex adaptive total variation model based on the gray level indicator for multiplicative noise removal, Abstract and Applied Analysis, 2013 (2013), 1-21. doi: 10.1155/2013/912373. Google Scholar
[16]	Y. Dong and T. Zeng, A convex variational model for restoring blurred images with multiplicative noise, SIAM Journal on Imaging Sciences, 6 (2013), 1598-1625. doi: 10.1137/120870621. Google Scholar
[17]	S. Durand, J. Fadili and M. Nikolova, Multiplicative noise removal using L1 fidelity on frame coefficients, Journal of Mathematical Imaging and Vision, 36 (2010), 201-226. doi: 10.1007/s10851-009-0180-z. Google Scholar
[18]	E. Esser, X. Zhang and T. F. Chan, A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science, Siam Journal on Imaging Sciences, 3 (2010), 1015-1046. doi: 10.1137/09076934X. Google Scholar
[19]	K. Florian, B. Kristian, P. Thomas and S. Rudolf, Second order total generalized variation (TGV) for MRI, Magnetic Resonance in Medicine, 65 (2011), 480-491. doi: 10.1002/mrm.22595. Google Scholar
[20]	E. Giusti, Minimal surfaces and functions of bounded variation, Monographs in Mathematics, Birkhäuser Verlag, Basel, 80 1984, 7-26. doi: 10.1007/978-1-4684-9486-0. Google Scholar
[21]	T. Goldstein and S. Osher, The split Bregman method for L1-regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343. doi: 10.1137/080725891. Google Scholar
[22]	H. Na, M. Kang, M. Jung and M. Kang, Nonconvex TGV regularization model for multiplicative noise removal with spatially varying parameters, Inverse Problems and Imaging, 13 (2019), 117-147. doi: 10.3934/ipi.2019007. Google Scholar
[23]	P. Kornprobst, R. Deriche and G. Aubert, Image sequence analysis via partial differential equations, Journal of Mathematical Imaging and Vision, 11 (1999), 5-26. doi: 10.1023/A:1008318126505. Google Scholar
[24]	K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 16 (2007), 2080-2095. doi: 10.1109/TIP.2007.901238. Google Scholar
[25]	T. Le, R. Chartrand and T. J. Asaki, A variational approach to reconstructing images corrupted by poisson noise, Journal of Mathematical Imaging and Vision, 27 (2007), 257-263. doi: 10.1007/s10851-007-0652-y. Google Scholar
[26]	S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage, IEEE Transactions on Geoscience and Remote Sensing, 50 (2012), 606-616. doi: 10.1109/TGRS.2011.2161586. Google Scholar
[27]	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639. doi: 10.1109/34.56205. Google Scholar
[28]	T. Pock, D. Cremers, H. Bischof and A. Chambolle, An algorithm for minimizing the mumford-Shah functional, IEEE International Conference on Computer Vision, (2009), 1133-1140. doi: 10.1109/ICCV.2009.5459348. Google Scholar
[29]	L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F. Google Scholar
[30]	C. Poynton, Digital Video and HD: Algorithms and Interfaces, 2$^nd$ edition, Elsevier, USA, 2012. doi: 10.1016/C2010-0-68987-5. Google Scholar
[31]	X. Shan, J. Sun and Z. Guo, Multiplicative noise removal based on the smooth diffusion equation, Journal of Mathematical Imaging and Vision, 61 (2019), 763-779. doi: 10.1007/s10851-018-00870-z. Google Scholar
[32]	R. Soorajkumar, P. K. Kumar, D. Girish and J. Rajan, Fourth order PDE based ultrasound despeckling using ENI classification, IEEE International Conference on Signal Processing and Communications (SPCOM), (2016). doi: 10.1109/SPCOM.2016.7746633. Google Scholar
[33]	D. M. Strong and T. F. Chan, Spatially and scale adaptive total variation based regularization and anisotropic diffusion in image processing, Journal of Mathematical Imaging and Vision, (1996). Google Scholar
[34]	M. Tur, K. C. Chin and J. W. Goodman, When is speckle noise multiplicative?, Applied Optics, 21 (1982), 1157-1159. doi: 10.1364/AO.21.001157. Google Scholar
[35]	L. Verdoliva, R. Gaetano, G. Ruello and G. Poggi, Optical-driven nonlocal SAR despeckling, IEEE Geoscience and Remote Sensing Letters, 12 (2015), 314-318. doi: 10.1109/LGRS.2014.2337515. Google Scholar
[36]	Z. Wang, A. C. Bovik and H. R. Sheikh, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, 53 (2015), 2765-2774. Google Scholar
[37]	J. Weickert, Anisotropic Diffusion in Image Processing, Teubner Stuttgart, ECMI Series, Teubner, Stuttgart, 1998. Google Scholar
[38]	C. Wu and X.-C. Tai, Augmented Lagrangian method, dual Methods, and split Bregman iteration for ROF, vectorial TV, and high order models, SIAM Journal on Imaging Sciences, 3 (2010), 300-339. doi: 10.1137/090767558. Google Scholar
[39]	J.-H. Yang, X.-L. Zhao, T.-H. Ma, Y. Chen, T.-Z. Huang and M. Ding, Emote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization, Journal of Computational and Applied Mathematics, 363 (2020), 124-144. doi: 10.1016/j.cam.2019.06.004. Google Scholar
[40]	X.-L. Zhao, F. Wang and M. K. Ng, A new convex optimization model for multiplicative noise and blur removal, SIAM Journal on Imagingences, 7 (2014), 456-475. doi: 10.1137/13092472X. Google Scholar
[41]	Y. Zhao, J. G. Liu, B. Zhang, W. Hong and Y.-R. Wu, Adaptive total variation regularization based SAR image despeckling and despeckling evaluation index, IEEE Transactions on Geoscience and Remote Sensing, 53 (2015), 2765-2774. doi: 10.1109/TGRS.2014.2364525. Google Scholar
[42]	Z. Zhou, Z. Guo, G. Dong, J. Sun, D. Zhang and B. Wu, A doubly degenerate diffusion model based on the gray level indicator for multiplicative noise removal, IEEE Transactions on Image Processing, 24 (2015), 249-260. doi: 10.1109/TIP.2014.2376185. Google Scholar

Figure 1. The relation between the influence of the noise and the gray levels. (a)1D original ladder-shaped signal; (b)1D speckle noise with mean of 1 and $ L = 4 $; (c) the comparison of original signal and speckle-corrupted signal; (d) The gray area shows values between the first and third quartiles, the dots represent the expectation and the blue line a single noisy realization. Expectation and quartiles are computed from 10000 noisy realizations

Download full-size image

Download as PowerPoint slide

Figure 2. Pretreatment effect map. The black curve is the original signal, and the green curve is the observed signal which has polluted by the Gamma noise with a standard deviation of $ 1/\sqrt{6} $ ($ L $ = 6). The red curve is result of preprocessing

Download full-size image

Download as PowerPoint slide

Figure 3. Adjustment for $ \alpha $. In the first row, the red curves are the observed signal which have polluted by the Gamma noise with a standard deviation of 1/2 (L = 4). In the second line, the blue and green curves represent the corresponding $ \alpha $ and $ \alpha^{'} $ respectively

Download full-size image

Download as PowerPoint slide

Figure 4. Comparison of AA, DZ and our model for multiplicative noise removal. The red curve is the denoising relization of the observed signals by each model. The random Gamma noise with a standard deviation of 1/$ \sqrt{6} $ ($ L $ = 6)

Download full-size image

Download as PowerPoint slide

Figure 5. Comparison of AA, DZ and our model for multiplicative noise removal. The random Gamma noise with a mean of 1 and a standard deviation of 1/2 (L = 4)

Download full-size image

Download as PowerPoint slide

Figure 6. Pie image (214 $ \times $ 216, $ L $ = 1.)

Download full-size image

Download as PowerPoint slide

Figure 7. Pie image (214 $ \times $ 216, $ L $=4.)

Download full-size image

Download as PowerPoint slide

Figure 8. Pie image (214 $ \times $ 216, $ L $ = 10.)

Download full-size image

Download as PowerPoint slide

Figure 9. Cameraman image (256 $ \times $ 256, $ L $ = 1)

Download full-size image

Download as PowerPoint slide

Figure 10. Cameraman image (256 $ \times $ 256, $ L $=4)

Download full-size image

Download as PowerPoint slide

Figure 11. Cameraman image (256 $ \times $ 256, $ L $ = 10)

Download full-size image

Download as PowerPoint slide

Figure 12. Parrot image (512 $ \times $ 512, $ L $ = 1.)

Download full-size image

Download as PowerPoint slide

Figure 13. Parrot image (512 $ \times $ 512, $ L $=4.)

Download full-size image

Download as PowerPoint slide

Figure 14. Parrot image (512 $ \times $ 512, $ L $ = 10.)

Download full-size image

Download as PowerPoint slide

Figure 15. Synthetic image (300 $ \times $ 300, $ L $ = 1.)

Download full-size image

Download as PowerPoint slide

Figure 16. Synthetic image (300 $ \times $ 300, $ L $=4.)

Download full-size image

Download as PowerPoint slide

Figure 17. Synthetic image (300 $ \times $ 300, $ L $ = 10.)

Download full-size image

Download as PowerPoint slide

Figure 18. Piglet image ($ 341 \times 199 $, $ L $ = 1.)

Download full-size image

Download as PowerPoint slide

Figure 19. Piglet image ($ 341 \times 199 $, $ L $=4.)

Download full-size image

Download as PowerPoint slide

Figure 20. Piglet image ($ 341 \times 199 $, $ L $ = 10.)

Download full-size image

Download as PowerPoint slide

Figure 21. Plots of Energy and PSNR of $ \tilde{u}^{k} $. The first column is about Pie(L=10) and the second column is about Cameraman(L=4)

Download full-size image

Download as PowerPoint slide

Table 1. Parameters used in the comparison study

	Parameters
Algorithm	$ L=1 $	$ L=4 $	$ L=10 $
The Pie image (214 $ \times $ 216)
AA ($ \lambda $)	5e-4	0.2	22
DZ ($ \alpha,\lambda $)	0.3, 0.2	0.5, 0.04	5, 0.03
$ E_{1} $-FDM ($ \lambda,\beta,p $)	3e-7, 3e-8, 1.3	2e-3, 1e-3, 1.3	2e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-7, 1e-8, 1.39, 0.74	2e-3, 1e-3, 1.39, 0.74	2e-3, 3e-3, 1.39, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 6e-3, 1.3	4e-3, 5e-3, 1.3	3e-3, 6e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-2, 6e-3, 1.39, 0.74	4e-3, 5e-3, 1.38, 0.74	4e-3, 4e-3, 1.34, 0.74
The Cameraman image($ 256\times256 $)
AA ($ \lambda $)	0.18	40	75
DZ ($ \alpha,\lambda $)	0.21, 0.25	1.6, 0.11	2.5, 0.03
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 5e-7, 1.3	2e-3, 2e-3, 1.3	3e-2, 2e-2, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	3e-7, 2e-7, 1.3, 0.8	3e-3, 2e-3, 1.33, 0.76	3e-2, 2e-2, 1.4, 0.78
$ E_{1} $-PDM ($ \lambda,\beta,p $)	6e-3, 8e-3, 1.3	8e-3, 6e-3, 1.3	8e-3, 3e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	1e-2, 6e-3, 1.3, 0.79	8e-3, 4e-3, 1.3, 0.75	6e-3, 6e-3, 1.3, 0.75
The Parrot image($ 512\times512 $)
AA ($ \lambda $)	0.6	1.5	8
DZ ($ \alpha,\lambda $)	0.11, 0.2	1, 0.2	15, 0.3
$ E_{1} $-FDM ($ \lambda,\beta,p $)	2e-6, 2e-6, 1.3	1e-4, 1e-4, 1.3	3e-3, 3e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-6, 1e-6, 1.36, 0.74	1e-4, 1e-4, 1.35, 0.74	1e-3, 1e-3, 1.32, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	6e-3, 6e-3, 1.3	6e-3, 6e-3, 1.3	6e-3, 6e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	6e-3, 6e-3, 1.36, 0.74	6e-3, 6e-3, 1.34, 0.74	6e-3, 6e-3, 1.32, 0.74
The Synthetic image($ 300\times300 $)
AA ($ \lambda $)	7e-4	4e-3	1e-3
DZ ($ \alpha,\lambda $)	0.117, 0.125	0.155, 0.1	0.11, 0.2
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 1e-7, 1.3	2e-4, 2e-4, 1.3	1e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-7, 1e-7, 1.35, 0.78	1e-4, 1e-4, 1.36, 0.74	1e-3, 1e-3, 1.35, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 5e-3, 1.3	4e-3, 4e-3, 1.3	4e-3, 4e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-3, 5e-3, 1.4, 0.75	4e-3, 5e-3, 1.4, 0.74	4e-3, 4e-3, 1.32, 0.74
The Piglet image($ 341\times199 $)
AA ($ \lambda $)	5e-3	80	190
DZ ($ \alpha,\lambda $)	3e-3, 0.16	2e-3, 3e-2	0.1, 7e-3
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 9e-8, 1.3	1e-4, 1e-4, 1.3	1e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	2e-7, 9e-8, 1.38, 0.74	1e-4, 1e-4, 1.37, 0.74	1e-3, 1e-3, 1.39, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 4e-3, 1.3	4e-3, 7e-3, 1.3	6e-3, 8e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-3, 7e-3, 1.35, 0.74	4e-3, 7e-3, 1.35, 0.74	6e-3, 8e-3, 1.32, 0.74

Download as excel

	Parameters
Algorithm	$ L=1 $	$ L=4 $	$ L=10 $
The Pie image (214 $ \times $ 216)
AA ($ \lambda $)	5e-4	0.2	22
DZ ($ \alpha,\lambda $)	0.3, 0.2	0.5, 0.04	5, 0.03
$ E_{1} $-FDM ($ \lambda,\beta,p $)	3e-7, 3e-8, 1.3	2e-3, 1e-3, 1.3	2e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-7, 1e-8, 1.39, 0.74	2e-3, 1e-3, 1.39, 0.74	2e-3, 3e-3, 1.39, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 6e-3, 1.3	4e-3, 5e-3, 1.3	3e-3, 6e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-2, 6e-3, 1.39, 0.74	4e-3, 5e-3, 1.38, 0.74	4e-3, 4e-3, 1.34, 0.74
The Cameraman image($ 256\times256 $)
AA ($ \lambda $)	0.18	40	75
DZ ($ \alpha,\lambda $)	0.21, 0.25	1.6, 0.11	2.5, 0.03
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 5e-7, 1.3	2e-3, 2e-3, 1.3	3e-2, 2e-2, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	3e-7, 2e-7, 1.3, 0.8	3e-3, 2e-3, 1.33, 0.76	3e-2, 2e-2, 1.4, 0.78
$ E_{1} $-PDM ($ \lambda,\beta,p $)	6e-3, 8e-3, 1.3	8e-3, 6e-3, 1.3	8e-3, 3e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	1e-2, 6e-3, 1.3, 0.79	8e-3, 4e-3, 1.3, 0.75	6e-3, 6e-3, 1.3, 0.75
The Parrot image($ 512\times512 $)
AA ($ \lambda $)	0.6	1.5	8
DZ ($ \alpha,\lambda $)	0.11, 0.2	1, 0.2	15, 0.3
$ E_{1} $-FDM ($ \lambda,\beta,p $)	2e-6, 2e-6, 1.3	1e-4, 1e-4, 1.3	3e-3, 3e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-6, 1e-6, 1.36, 0.74	1e-4, 1e-4, 1.35, 0.74	1e-3, 1e-3, 1.32, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	6e-3, 6e-3, 1.3	6e-3, 6e-3, 1.3	6e-3, 6e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	6e-3, 6e-3, 1.36, 0.74	6e-3, 6e-3, 1.34, 0.74	6e-3, 6e-3, 1.32, 0.74
The Synthetic image($ 300\times300 $)
AA ($ \lambda $)	7e-4	4e-3	1e-3
DZ ($ \alpha,\lambda $)	0.117, 0.125	0.155, 0.1	0.11, 0.2
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 1e-7, 1.3	2e-4, 2e-4, 1.3	1e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	1e-7, 1e-7, 1.35, 0.78	1e-4, 1e-4, 1.36, 0.74	1e-3, 1e-3, 1.35, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 5e-3, 1.3	4e-3, 4e-3, 1.3	4e-3, 4e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-3, 5e-3, 1.4, 0.75	4e-3, 5e-3, 1.4, 0.74	4e-3, 4e-3, 1.32, 0.74
The Piglet image($ 341\times199 $)
AA ($ \lambda $)	5e-3	80	190
DZ ($ \alpha,\lambda $)	3e-3, 0.16	2e-3, 3e-2	0.1, 7e-3
$ E_{1} $-FDM ($ \lambda,\beta,p $)	1e-7, 9e-8, 1.3	1e-4, 1e-4, 1.3	1e-3, 1e-3, 1.3
$ E_{2} $-FDM ($ \lambda,\beta,p,a $)	2e-7, 9e-8, 1.38, 0.74	1e-4, 1e-4, 1.37, 0.74	1e-3, 1e-3, 1.39, 0.74
$ E_{1} $-PDM ($ \lambda,\beta,p $)	4e-3, 4e-3, 1.3	4e-3, 7e-3, 1.3	6e-3, 8e-3, 1.3
$ E_{2} $-PDM ($ \lambda,\beta,p,a $)	4e-3, 7e-3, 1.35, 0.74	4e-3, 7e-3, 1.35, 0.74	6e-3, 8e-3, 1.32, 0.74

Table 2. Comparison of PSNR, MAE, SSIM and iterations

	PSNR			MAE			SSIM			#iter
$ L $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $
The Pie image($ 214\times216 $)
AA	$ 13.12 $	$ 18.43 $	$ 22.20 $	$ 42.51 $	$ 22.66 $	$ 13.40 $	0.57	0.77	0.88	1978	743	464
DZ	$ 14.25 $	$ 19.94 $	$ 23.45 $	$ 32.77 $	$ 16.26 $	$ 10.14 $	0.63	0.86	0.92	637	1162	1027
$ E_{1} $-FDM	$ 17.26 $	$ 21.35 $	$ 24.00 $	$ 23.71 $	$ 14.33 $	10.15	0.67	0.84	0.90	481	275	273
$ E_{2} $-FDM	$ 17.45 $	$ 21.30 $	24.08	$ 23.50 $	$ 14.44 $	10.10	0.69	0.84	0.90	362	256	211
$ E_{1} $-PDM	$ 17.22 $	$ 21.30 $	$ 23.86 $	$ 24.00 $	$ 14.29 $	$ 10.15 $	0.71	0.84	0.90	1973	609	312
$ E_{2} $-PDM	$ 17.46 $	$ 21.21 $	$ 23.87 $	$ 23.19 $	$ 14.53 $	$ 10.28 $	0.71	0.83	0.90	1475	485	239
The cameraman image($ 256\times256 $)
AA	19.06	22.96	25.09	16.79	10.09	7.94	0.56	0.69	0.74	1800	672	351
DZ	19.34	22.81	25.22	16.64	10.45	7.94	0.53	0.70	0.75	387	291	338
$ E_{1} $-FDM	21.56	24.44	26.37	12.95	9.19	7.29	0.63	0.71	0.77	442	361	289
$ E_{2} $-FDM	21.56	24.50	26.42	12.98	9.18	7.27	0.63	0.71	0.76	395	315	238
$ E_{1} $-PDM	21.70	24.28	26.21	12.60	9.01	7.29	0.65	0.73	0.78	1469	455	243
$ E_{2} $-PDM	21.76	24.30	26.23	11.96	9.00	7.35	0.66	0.73	0.77	1167	382	203
The Parrot image($ 512\times512 $)
AA	20.52	24.46	26.89	14.73	10.15	7.86	0.60	0.68	0.72	1833	666	359
DZ	20.59	25.19	27.52	14.22	9.38	7.29	0.57	0.71	0.76	533	187	69
$ E_{1} $-FDM	23.84	26.73	28.57	10.88	7.95	6.49	0.66	0.74	0.78	445	391	342
$ E_{2} $-FDM	23.84	26.75	28.61	10.96	8.00	6.48	0.65	0.73	0.78	372	309	227
$ E_{1} $-PDM	23.88	26.73	28.55	10.93	7.94	6.50	0.65	0.73	0.78	1907	629	323
$ E_{2} $-PDM	23.81	26.71	28.60	11.96	9.00	6.48	0.65	0.73	0.78	1572	523	240
The Synthetic image($ 300\times300 $)
AA	$ 22.31 $	$ 27.44 $	$ 30.43 $	$ 11.90 $	$ 6.32 $	$ 4.35 $	0.81	0.88	0.92	2664	1010	643
DZ	$ 22.43 $	$ 27.90 $	$ 30.54 $	$ 10.30 $	$ 5.42 $	$ 4.04 $	0.77	0.94	0.96	1195	570	291
$ E_{1} $-FDM	$ 24.71 $	$ 29.08 $	$ 31.68 $	$ 9.02 $	$ 4.88 $	3.30	0.84	0.93	0.95	507	442	371
$ E_{2} $-FDM	$ 24.77 $	$ 29.07 $	31.71	$ 8.61 $	$ 4.94 $	3.41	0.83	0.92	0.95	457	337	282
$ E_{1} $-PDM	$ 25.44 $	$ 28.89 $	$ 31.46 $	$ 7.30 $	$ 4.29 $	$ 3.24 $	0.89	0.93	0.96	2506	787	425
$ E_{2} $-PDM	$ 25.36 $	$ 28.93 $	$ 31.42 $	$ 7.43 $	$ 4.43 $	$ 3.24 $	0.89	0.92	0.96	1759	645	338
The Piglet image($ 341 \times 199 $)
AA	$ 21.70 $	$ 26.96 $	$ 30.08 $	$ 12.27 $	$ 6.88 $	$ 4.72 $	0.83	0.89	0.91	2363	1068	660
DZ	$ 21.74 $	$ 27.17 $	$ 30.18 $	$ 11.94 $	$ 6.67 $	$ 4.66 $	0.73	0.89	0.91	739	1540	4440
$ E_{1} $-FDM	$ 24.92 $	$ 28.45 $	$ 30.89 $	$ 9.65 $	$ 6.08 $	4.53	0.82	0.89	0.92	499	390	345
$ E_{2} $-FDM	$ 24.82 $	$ 28.51 $	30.75	$ 9.44 $	$ 5.88 $	4.58	0.78	0.88	0.91	432	321	297
$ E_{1} $-PDM	$ 24.96 $	$ 28.33 $	$ 30.68 $	$ 8.99 $	$ 5.95 $	$ 4.58 $	0.84	0.88	0.91	1904	666	335
$ E_{2} $-PDM	$ 24.89 $	$ 28.31 $	$ 30.68 $	$ 9.12 $	$ 5.96 $	$ 4.46 $	0.80	0.88	0.92	1463	530	280

Download as excel

	PSNR			MAE			SSIM			#iter
$ L $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $	$ 1 $	$ 4 $	$ 10 $
The Pie image($ 214\times216 $)
AA	$ 13.12 $	$ 18.43 $	$ 22.20 $	$ 42.51 $	$ 22.66 $	$ 13.40 $	0.57	0.77	0.88	1978	743	464
DZ	$ 14.25 $	$ 19.94 $	$ 23.45 $	$ 32.77 $	$ 16.26 $	$ 10.14 $	0.63	0.86	0.92	637	1162	1027
$ E_{1} $-FDM	$ 17.26 $	$ 21.35 $	$ 24.00 $	$ 23.71 $	$ 14.33 $	10.15	0.67	0.84	0.90	481	275	273
$ E_{2} $-FDM	$ 17.45 $	$ 21.30 $	24.08	$ 23.50 $	$ 14.44 $	10.10	0.69	0.84	0.90	362	256	211
$ E_{1} $-PDM	$ 17.22 $	$ 21.30 $	$ 23.86 $	$ 24.00 $	$ 14.29 $	$ 10.15 $	0.71	0.84	0.90	1973	609	312
$ E_{2} $-PDM	$ 17.46 $	$ 21.21 $	$ 23.87 $	$ 23.19 $	$ 14.53 $	$ 10.28 $	0.71	0.83	0.90	1475	485	239
The cameraman image($ 256\times256 $)
AA	19.06	22.96	25.09	16.79	10.09	7.94	0.56	0.69	0.74	1800	672	351
DZ	19.34	22.81	25.22	16.64	10.45	7.94	0.53	0.70	0.75	387	291	338
$ E_{1} $-FDM	21.56	24.44	26.37	12.95	9.19	7.29	0.63	0.71	0.77	442	361	289
$ E_{2} $-FDM	21.56	24.50	26.42	12.98	9.18	7.27	0.63	0.71	0.76	395	315	238
$ E_{1} $-PDM	21.70	24.28	26.21	12.60	9.01	7.29	0.65	0.73	0.78	1469	455	243
$ E_{2} $-PDM	21.76	24.30	26.23	11.96	9.00	7.35	0.66	0.73	0.77	1167	382	203
The Parrot image($ 512\times512 $)
AA	20.52	24.46	26.89	14.73	10.15	7.86	0.60	0.68	0.72	1833	666	359
DZ	20.59	25.19	27.52	14.22	9.38	7.29	0.57	0.71	0.76	533	187	69
$ E_{1} $-FDM	23.84	26.73	28.57	10.88	7.95	6.49	0.66	0.74	0.78	445	391	342
$ E_{2} $-FDM	23.84	26.75	28.61	10.96	8.00	6.48	0.65	0.73	0.78	372	309	227
$ E_{1} $-PDM	23.88	26.73	28.55	10.93	7.94	6.50	0.65	0.73	0.78	1907	629	323
$ E_{2} $-PDM	23.81	26.71	28.60	11.96	9.00	6.48	0.65	0.73	0.78	1572	523	240
The Synthetic image($ 300\times300 $)
AA	$ 22.31 $	$ 27.44 $	$ 30.43 $	$ 11.90 $	$ 6.32 $	$ 4.35 $	0.81	0.88	0.92	2664	1010	643
DZ	$ 22.43 $	$ 27.90 $	$ 30.54 $	$ 10.30 $	$ 5.42 $	$ 4.04 $	0.77	0.94	0.96	1195	570	291
$ E_{1} $-FDM	$ 24.71 $	$ 29.08 $	$ 31.68 $	$ 9.02 $	$ 4.88 $	3.30	0.84	0.93	0.95	507	442	371
$ E_{2} $-FDM	$ 24.77 $	$ 29.07 $	31.71	$ 8.61 $	$ 4.94 $	3.41	0.83	0.92	0.95	457	337	282
$ E_{1} $-PDM	$ 25.44 $	$ 28.89 $	$ 31.46 $	$ 7.30 $	$ 4.29 $	$ 3.24 $	0.89	0.93	0.96	2506	787	425
$ E_{2} $-PDM	$ 25.36 $	$ 28.93 $	$ 31.42 $	$ 7.43 $	$ 4.43 $	$ 3.24 $	0.89	0.92	0.96	1759	645	338
The Piglet image($ 341 \times 199 $)
AA	$ 21.70 $	$ 26.96 $	$ 30.08 $	$ 12.27 $	$ 6.88 $	$ 4.72 $	0.83	0.89	0.91	2363	1068	660
DZ	$ 21.74 $	$ 27.17 $	$ 30.18 $	$ 11.94 $	$ 6.67 $	$ 4.66 $	0.73	0.89	0.91	739	1540	4440
$ E_{1} $-FDM	$ 24.92 $	$ 28.45 $	$ 30.89 $	$ 9.65 $	$ 6.08 $	4.53	0.82	0.89	0.92	499	390	345
$ E_{2} $-FDM	$ 24.82 $	$ 28.51 $	30.75	$ 9.44 $	$ 5.88 $	4.58	0.78	0.88	0.91	432	321	297
$ E_{1} $-PDM	$ 24.96 $	$ 28.33 $	$ 30.68 $	$ 8.99 $	$ 5.95 $	$ 4.58 $	0.84	0.88	0.91	1904	666	335
$ E_{2} $-PDM	$ 24.89 $	$ 28.31 $	$ 30.68 $	$ 9.12 $	$ 5.96 $	$ 4.46 $	0.80	0.88	0.92	1463	530	280

[1]	Xiaojing Ye, Haomin Zhou. Fast total variation wavelet inpainting via approximated primal-dual hybrid gradient algorithm. Inverse Problems & Imaging, 2013, 7 (3) : 1031-1050. doi: 10.3934/ipi.2013.7.1031
[2]	Gianni Di Pillo, Giampaolo Liuzzi, Stefano Lucidi. A primal-dual algorithm for nonlinear programming exploiting negative curvature directions. Numerical Algebra, Control & Optimization, 2011, 1 (3) : 509-528. doi: 10.3934/naco.2011.1.509
[3]	Mujibur Rahman Chowdhury, Jun Zhang, Jing Qin, Yifei Lou. Poisson image denoising based on fractional-order total variation. Inverse Problems & Imaging, 2020, 14 (1) : 77-96. doi: 10.3934/ipi.2019064
[4]	Haijuan Hu, Jacques Froment, Baoyan Wang, Xiequan Fan. Spatial-Frequency domain nonlocal total variation for image denoising. Inverse Problems & Imaging, 2020, 14 (6) : 1157-1184. doi: 10.3934/ipi.2020059
[5]	Yu-Hong Dai, Zhouhong Wang, Fengmin Xu. A Primal-dual algorithm for unfolding neutron energy spectrum from multiple activation foils. Journal of Industrial & Management Optimization, 2021, 17 (5) : 2367-2387. doi: 10.3934/jimo.2020073
[6]	Xavier Bresson, Tony F. Chan. Fast dual minimization of the vectorial total variation norm and applications to color image processing. Inverse Problems & Imaging, 2008, 2 (4) : 455-484. doi: 10.3934/ipi.2008.2.455
[7]	Michael Hintermüller, Monserrat Rincon-Camacho. An adaptive finite element method in $L^2$-TV-based image denoising. Inverse Problems & Imaging, 2014, 8 (3) : 685-711. doi: 10.3934/ipi.2014.8.685
[8]	Jen-Yen Lin, Hui-Ju Chen, Ruey-Lin Sheu. Augmented Lagrange primal-dual approach for generalized fractional programming problems. Journal of Industrial & Management Optimization, 2013, 9 (4) : 723-741. doi: 10.3934/jimo.2013.9.723
[9]	Ke Chen, Yiqiu Dong, Michael Hintermüller. A nonlinear multigrid solver with line Gauss-Seidel-semismooth-Newton smoother for the Fenchel pre-dual in total variation based image restoration. Inverse Problems & Imaging, 2011, 5 (2) : 323-339. doi: 10.3934/ipi.2011.5.323
[10]	Yunho Kim, Paul M. Thompson, Luminita A. Vese. HARDI data denoising using vectorial total variation and logarithmic barrier. Inverse Problems & Imaging, 2010, 4 (2) : 273-310. doi: 10.3934/ipi.2010.4.273
[11]	Fengmin Wang, Dachuan Xu, Donglei Du, Chenchen Wu. Primal-dual approximation algorithms for submodular cost set cover problems with linear/submodular penalties. Numerical Algebra, Control & Optimization, 2015, 5 (2) : 91-100. doi: 10.3934/naco.2015.5.91
[12]	Yu-Hong Dai, Xin-Wei Liu, Jie Sun. A primal-dual interior-point method capable of rapidly detecting infeasibility for nonlinear programs. Journal of Industrial & Management Optimization, 2020, 16 (2) : 1009-1035. doi: 10.3934/jimo.2018190
[13]	Yanqin Bai, Xuerui Gao, Guoqiang Wang. Primal-dual interior-point algorithms for convex quadratic circular cone optimization. Numerical Algebra, Control & Optimization, 2015, 5 (2) : 211-231. doi: 10.3934/naco.2015.5.211
[14]	Siqi Li, Weiyi Qian. Analysis of complexity of primal-dual interior-point algorithms based on a new kernel function for linear optimization. Numerical Algebra, Control & Optimization, 2015, 5 (1) : 37-46. doi: 10.3934/naco.2015.5.37
[15]	Yixuan Yang, Yuchao Tang, Meng Wen, Tieyong Zeng. Preconditioned Douglas-Rachford type primal-dual method for solving composite monotone inclusion problems with applications. Inverse Problems & Imaging, 2021, 15 (4) : 787-825. doi: 10.3934/ipi.2021014
[16]	Yunhai Xiao, Junfeng Yang, Xiaoming Yuan. Alternating algorithms for total variation image reconstruction from random projections. Inverse Problems & Imaging, 2012, 6 (3) : 547-563. doi: 10.3934/ipi.2012.6.547
[17]	Juan C. Moreno, V. B. Surya Prasath, João C. Neves. Color image processing by vectorial total variation with gradient channels coupling. Inverse Problems & Imaging, 2016, 10 (2) : 461-497. doi: 10.3934/ipi.2016008
[18]	Zhengmeng Jin, Chen Zhou, Michael K. Ng. A coupled total variation model with curvature driven for image colorization. Inverse Problems & Imaging, 2016, 10 (4) : 1037-1055. doi: 10.3934/ipi.2016031
[19]	Zhiguang Zhang, Qiang Liu, Tianling Gao. A fast explicit diffusion algorithm of fractional order anisotropic diffusion for image denoising. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2021018
[20]	Rongliang Chen, Jizu Huang, Xiao-Chuan Cai. A parallel domain decomposition algorithm for large scale image denoising. Inverse Problems & Imaging, 2019, 13 (6) : 1259-1282. doi: 10.3934/ipi.2019055

2019 Impact Factor: 1.373

Tools

Metrics

Other articles
by authors

on AIMS
Yu Zhang Songsong Li Zhichang Guo Boying Wu
on Google Scholar
Yu Zhang Songsong Li Zhichang Guo Boying Wu

2019 Impact Factor: 1.373

Tools

Article outline

Figures and Tables

2019 Impact Factor: 1.373

Tools

Figures and Tables

2019 Impact Factor: 1.373

Tools

Metrics

Other articles
by authors

on AIMS
Yu Zhang Songsong Li Zhichang Guo Boying Wu
on Google Scholar
Yu Zhang Songsong Li Zhichang Guo Boying Wu

[Back to Top]