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http://dx.doi.org/10.3837/tiis.2020.09.010

Low-Rank Representation-Based Image Super-Resolution Reconstruction with Edge-Preserving  

Gao, Rui (School of Information and Control Engineering, China University of Mining and Technology)
Cheng, Deqiang (School of Information and Control Engineering, China University of Mining and Technology)
Yao, Jie (School of Information and Control Engineering, China University of Mining and Technology)
Chen, Liangliang (School of Information and Control Engineering, China University of Mining and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.9, 2020 , pp. 3745-3761 More about this Journal
Abstract
Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.
Keywords
super-resolution; gradient domain guided image filter; low-rank representation; sparse representation;
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1 H. Chang, D-Y. Yeung, and Y. Xiong, "Super-resolution through neighbor embedding," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 275-282, Jul. 2004.
2 W. Dong, L. Zhang, G. Shi, and X. Wu, "Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization," IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 1838-1857, Jul. 2011.   DOI
3 Y. Tang, W. Gong, Q. Yi, and W. Li, "Combining sparse coding with structured output regression machine for single image super-resolution," Information Science, vol. 430-431, pp. 577-598, 2018.   DOI
4 R. Timofte, V. D. Smet, and L. V. Gool, "Anchored neighborhood regression for fast example-based super-resolution," in Proc. of IEEE International Conf. on Computer Vision, pp. 1920-1927, Dec. 2013.
5 R. Timofte, V. D. Smet, and L. V. Gool, "A+: Adjusted anchored neighborhood regression for fast super-resolution." in Proc. of Asian Conf. on Computer Vision, pp. 111-126, 2014.
6 C. Zhang, W. Liu, J. Liu, C. Liu, and C. Shi, "Sparse representation and adaptive mixed samples regression for single image super-resolution," Signal Processing: Image Communication, vol. 67, pp. 79-89, 2018.   DOI
7 D. Cheng, L. Chen, Y. Cai, L. You, and Y. Tu, "Image super-resolution reconstruction based on multi-dictionary and edge fusion," Journal of China Coal Society, vol. 43, no. 7, pp. 2084-2090, Jul. 2018.
8 C. Ren, X. He, and T.Q. Nguyen, "Single image super-resolution via adaptive high-dimensional non-local total variation and adaptive geometric feature," IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 90-106, Jan. 2017.   DOI
9 B. Li, H. Chang, S. Shan, and X. Chen, "Locality preserving constraints for super-resolution with neighbor embedding," in Proc. of IEEE International Conf. on Image Processing, pp. 1189-1192, 2009.
10 Y. Zhang, Q. Fan, F. Bao, Y. Liu, and C. Zhang, "Single-image super-resolution based on rational fractal interpolation," IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 3782-3797, Aug. 2018.   DOI
11 X. Li and M.T. Orchard, "New edge-directed interpolation," IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1521-1527, Oct. 2001.   DOI
12 F. Shi, J. Cheng, L. Wang, P-T. Yap, and D.G. Shen, "LRTV: MR image super-resolution with low-rank and total variation regularizations," IEEE Transactions on Medical Imaging, vol. 34, no. 12, pp. 2459-2466, Dec. 2015.   DOI
13 K. Chang, P. L. K. Ding, and B. Li, "Single image super-resolution using collaborative representation and non-local self-similarity," Signal Processing, vol. 149, pp. 49-61, Feb. 2018.   DOI
14 Y-W. Tai, S. Liu, M. S. Brown, and S. Lin, "Super resolution using edge prior and single image detail synthesis," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2400-2407, 2010.
15 K. Zhang, X. Gao, J. Li, and H. Xia, "Single image super-resolution using regularization of non-local steering kernel regression," Signal Processing, vol. 123, pp. 53-63, Nov. 2016.   DOI
16 K. Zhang, X. Gao, D. Tao, and X. Li, "Single image super-resolution with non-local means and steering kernel regression," IEEE Transactions on Image Processing, vol. 21, no.11, pp. 4544-4556, Nov. 2012.   DOI
17 S. Dai, M. Han, W. Xu, Y. Wu, Y. Gong, and A. K. Katsaggelos, "SoftCuts: a soft edge smoothness prior for color image super-resolution," IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 969-981, May. 2009.   DOI
18 J. Sun, Z. Xu, and H-Y. Shum, "Gradient profile prior and its applications in image super-resolution and enhancement," IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1529-1542, Jun. 2011.   DOI
19 Q. Yan, Y. Xu, X. Yang, and T. Q. Nguyen, "Single image super-resolution based on gradient profile sharpness," IEEE Transactions on Image Processing, vol. 24, no. 10, pp. 3187-3202, Oct. 2015.   DOI
20 H. Chen, X. He, Q. Teng and C. Ren, "Single image super resolution using local smoothness and nonlocal self-similarity priors," Signal Processing: Image Communication, vol. 43, pp. 68-81, Jan. 2016.   DOI
21 L. Chen, Q. Kou, D. Cheng, and J. Yao, "Content-guided deep residual network for single image super-resolution," Optik, vol. 202, Feb, 2020.
22 K. Zhang, X. Gao, X. Li, and D. Tao, "Partially supervised neighbor embedding for example-based image super-resolution," IEEE Journal Selected Topics Signal Processing, vol. 5, no. 2, pp. 230-239, Apr. 2011.   DOI
23 X. Gao, K. Zhang, D. Tao, and X. Li, "Image super-resolution with sparse neighbor embedding," IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3194-3205, Jul. 2012.   DOI
24 J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution as sparse representation of raw image patches," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, Jun. 2008.
25 J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2861-2873, Nov. 2010.   DOI
26 J. Jiang, X. Ma, C. Chen, T. Lu, Z. Wang, and J. Ma, "Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means," IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 15-26, Jan. 2017.   DOI
27 C. Dong, C. C. Loy, K. He, and X. Tang, "Image super-resolution using deep convolutional networks," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 38, no. 2, pp. 295-307, Feb. 2016.   DOI
28 K. Zeng, Jun Yu, R. Wang, C. Li, and D. Tao, "Coupled deep autoencoder for single image super-resolution," IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 27-37, Jan. 2017.   DOI
29 R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse-representations," in Proc. of 7th International Conf. on Curves Surfaces, pp. 711-730, 2010.
30 S. Huang, J. Sun, Y. Yang, Y. Fang, and P. Lin "Robust single-image super-resolution based on adaptive edge-preserving smoothing regularization," IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2650-2663, Jun. 2018.   DOI
31 F. Kou, W. Chen, C. Wen, and Z. Li, "Gradient domain guided image filtering," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4528-4539, Nov. 2015.   DOI
32 X. Chen and C. Qi, "Low-rank neighbor embedding for single image super-resolution," IEEE Signal Processing Letters, vol. 21, no. 1, pp. 79-82, Jan. 2014.   DOI
33 W. Dong, G. Shi, X. Li, Y. Ma, and F. Huang, "Compressive sensing via nonlocal low-rank regularization," IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3618-3632, Aug. 2014.   DOI
34 J. Zhao, H. Hu, and F. Cao, "Image super-resolution via adaptive sparse representation," Knowledge-Based-Systems, vol. 124, pp. 23-33, 2017.   DOI
35 X. Li, G. Gao, Y. Zhang, and B. Wang, "Single image super-resolution via adaptive sparse representation and low-rank constraint," Journal of Visual Communication & Image Representation, vol. 55, pp. 319-330, 2018.   DOI
36 T. Lu, Z. Xiong, Y. Zhang, B. Wang, and T. Lu, "Robust face super-resolution via locality-constrained low-rank representation," IEEE Access, 5, pp. 13103-13117, 2017.   DOI
37 W. Gong, Q. Li, Y. Tang, and W. Li, "Multi-layer strategy and reconstruction model with low rank and local rank regularizations for single image super-resolution," Signal Processing: Image Communication, vol. 57, 197-210, 2017.   DOI
38 N. Han, Z. Song, and Y. Li, "Cluster-based image super-resolution via jointly low-rank and sparse representation," Journal of Visual Communication & Image Representation, vol. 38, pp. 175-185, 2016.   DOI
39 J.Shi and C.Qi, "Low-rank representation for single image super-resolution via self-similarity learning," in Proc. of IEEE International Conf. on Image Processing, pp. 1424-1428, 2016.
40 G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, "Robust recovery of subspace structures by low-rank representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 1, pp. 171-184, Jan. 2013.   DOI
41 K. Tang, R. Liu, Z. Su, and J. Zhang, "Structure-constrained low-rank representation," IEEE Transactions on Neural Networks & Learning System, vol. 25, no. 12, pp. 2167-2179, Dec. 2014.   DOI
42 J. Xiao, J. Hays, K. A. Ehinger, and A. Torralba, "Sun database: large-scale scene recognition from abbey to zoo," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3485-3492, 2010.
43 J. Wen, B. Zhang, Y. Xu, J. Yang, and N. Han, "Adaptive weighted nonnegtive low-rank representation," Pattern Recognition, vol. 81, pp. 326-340, 2018.   DOI
44 S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, "Distributed optimization and statistical learning via the alternating direction method of multipliers," Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1-122, 2011.   DOI
45 E. Grave, G. Obozinski, and F. Bach, "Trace lasso: a trace norm regularization for correlated designs," in Proc. of Advances in Neural Information Processing Systems, pp. 2187-2195, 2011.