© The Institution of Engineering and Technology
This study proposes a novel super-resolution regularisation model based on adaptive sparse representation and self-learning frameworks. The fidelity term in the model ensures that the reconstructed image is consistent with the observation image. The adaptive sparsity regularisation term constrains the reconstructed image with an adaptive sparse representation, which successfully harmonises the sparse representation and the collaborative representation adaptively via producing suitable coefficients. To construct a more effective dictionary, the high-frequency features from the underlying image patches are extracted, and the dictionary learning and sparse representation are integrated. To this end, the alternating minimisation algorithm is used to divide this model into three subproblems, and the alternating direction method of multipliers and iterative back-projection method are used to solve the subproblems. To illustrate the effectiveness of the proposed method, additional experiments are conducted on some generic images. Compared with some state-of-the-art algorithms, the experimental results demonstrate that the proposed method achieves better results in terms of both visual quality and noise immunity.
References
-
-
1)
-
10. Sun, J., Sun, J., Xu, Z., et al: ‘Image super-resolution using gradient profile prior’, IEEE Comput. Vis. Pattern Recognit., 2008, 8, pp. 1–8.
-
2)
-
35. Cai, J.F., Candès, E.J., Shen, Z.W.: ‘A singular value thresholding algorithm for matrix completion’, SIAM J. Opt., 2010, 20, (4), pp. 1956–1982.
-
3)
-
7. Lysaker, M., Tai, X.: ‘Iterative image restoration combining total variation minimization and a second-order functional’, Int. J. Comput. Vis., 2006, 66, (1), pp. 5–18.
-
4)
-
6. Chan, T., Esedoglu, S., Yip, A.: ‘Recent development in total variation image restoration’, in Paragios, N., Chen, Y., Faugeras, O. (Eds.): Mathematical models in computer vision (Springer, New York, 2004).
-
5)
-
12. Gong, W., Hu, L., Li, J., et al: ‘Combining sparse representation and local rank constraint for single image super resolution’, Inf. Sci., 2015, 325, pp. 1–19.
-
6)
-
15. Liu, D., Wang, Z.W., Wen, B.H., et al: ‘Robust single image super-resolution via deep networks with sparse prior’, IEEE Trans. Image Process., 2016, 25, (7), pp. 3194–3207.
-
7)
-
16. Peleg, T., Elad, M.: ‘A statistical prediction model based on sparse representations for single image super-resolution’, IEEE Trans. Image Process., 2014, 23, (6), pp. 2569–2582.
-
8)
-
1. Wang, X.L., Liu, N.F.: ‘Super-resolution of remote sensing images via sparse structural manifold embedding’, Neurocomputing, 2016, 173, pp. 1402–1411.
-
9)
-
23. Freeman, W.T., Jones, T.R., Pasztor, E.C.: ‘Example-based super-resolution’, Comput. Graph. Appl., 2002, 22, (2), pp. 56–65.
-
10)
-
19. Shi, J., Chun, Q.: ‘Low-rank sparse representation for single image super-resolution via self-similarity learning’. Proc. IEEE Int. Conf. Image Process. (ICIP), Phoenix, Arizona, USA, 2016, pp. 1424–1428.
-
11)
-
14. Li, J., Gong, W., Li, W.: ‘Dual-sparsity regularized sparse representation for single image super-resolution’, Inf. Sci., 2015, 298, pp. 257–273.
-
12)
-
4. Nguyen, K., Fookes, C., Sridharan, S., et al: ‘Feature-domain super-resolution for iris recognition’, Comp. Vis. Image Underst., 2012, 117, (10), pp. 1526–1535.
-
13)
-
18. Zhao, J., Hu, H., Cao, F.: ‘Image super-resolution method via adaptive sparse representation’, Knowl.-Based Syst., 2017, 124, pp. 23–33.
-
14)
-
33. Irani, M., Peleg, S.: ‘Motion analysis for image enhancement: resolution, occlusion and transparency’, J. Vis. Commun. Image Represent., 1993, 4, (4), pp. 324–335.
-
15)
-
5. Keys, R.G.: ‘Cubic convolution interpolation for digital image processing’, Acoust., Speech, Signal Process., 2003, 29, (6), pp. 1153–1160.
-
16)
-
9. Dai, S., Han, M., Xu, W., et al: ‘Softcuts: a softedge smoothness prior for color image super-resolution’, IEEE Trans. Image Process., 2009, 18, (5), pp. 969–981.
-
17)
-
25. Zhang, Y., Zhang, Y., Zhang, J.: ‘Collaborative representation cascade for single-image super-resolution’, IEEE Trans. Syst. Man Cybern. Syst., 2017, 99, pp. 1–16.
-
18)
-
20. Li, J., Wu, J., Deng, H., et al: ‘A self-learning image super-resolution method via sparse representation and non-local similarity’, Neurocomputing, 2016, 184, (5), pp. 196–206.
-
19)
-
21. Timofte, R., De, V., Gool, L.V.: ‘Anchored neighborhood regression for fast example-based super-resolution’. Proc. IEEE Int. Conf. Computer Vision (ICCV), Sydney, NSW, 2013, pp. 1920–1927.
-
20)
-
29. Glasner, D., Bagon, S., Irani, M.: ‘Super-resolution from a single image’. Proc. IEEE Int. Conf. Computer Vision (ICCV), Kyoto, 2009, pp. 349–356.
-
21)
-
36. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error measurement to structural similarity’, IEEE Trans. Image Process., 2004, 3, (4), pp. 600–612.
-
22)
-
34. Grave, E., Obozinski, G.R., Bach, F.R.: ‘Trace lasso: a trace norm regularization for correlated designs’, Adv. Neur. Inf. Process. Syst. (NIPS), 2011, 24, pp. 2187–2195.
-
23)
-
28. Timofte, R., Gool, L.V.: ‘An adaptive and weighted collaborative representations for image classification’, Pattern Recognit. Lett., 2014, 43, (1), pp. 127–135.
-
24)
-
32. Yang, J., Yuan, X.: ‘Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization’, Math. Comp., 2013, 82, (281), pp. 301–329.
-
25)
-
11. Cao, F., Cai, M., Tan, Y., et al: ‘Image super-resolution via adaptive ℓp(0 < p < 1) regularization and sparse representation’, IEEE Trans. Neur. Netw. Learn. Syst., 2016, 27, (7), pp. 1550–1561.
-
26)
-
17. Yang, J., Wright, J., Huang, T.S., et al: ‘Image super-resolution via sparse representation’, IEEE. Trans. Image Process., 2010, 19, (11), pp. 2861–2873.
-
27)
-
2. Ibragimov, B., Prince, J.L., Murano, E.Z., et al: ‘Segmentation of tongue muscles from super-resolution magnetic resonance images’, Med. Image Anal., 2015, 20, (1), pp. 198–207.
-
28)
-
27. Dong, C., Chen, C.L., He, K., et al: ‘Image super-resolution using deep convolutional networks’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (2), pp. 295–307.
-
29)
-
3. Ming, Y.: ‘Robust regional bounding spherical descriptor for 3D face recognition and emotion analysis’, Image Vis. Comput., 2015, 35, (3), pp. 14–22.
-
30)
-
22. Yang, J., Wang, Z., Lin, Z., et al: ‘Couple dictionary training for image super-resolution’, IEEE. Trans. Image Process., 2012, 21, (8), pp. 3467–3478.
-
31)
-
31. Lin, Z., Liu, R., Su, Z.: ‘Linearized alternating direction method with adaptive penalty for low-rank representation’. Advances in Neural Information Processing Systems (NIPS), 2011, pp. 612–620.
-
32)
-
13. Han, N.N., Song, Z.J., Li, Y.: ‘Cluster-based image super-resolution via jointly low-rank and sparse representation’, J. Vis. Commun. Image Repres., 2016, 38, pp. 175–185.
-
33)
-
30. Lin, Z., Chen, M., Ma, Y., et al: ‘The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices’, .
-
34)
-
8. Marquina, A., Osher, S.J.: ‘Image super-resolution by TV-regularization and Bregman iteration’, J. Sci. Comput., 2008, 37, pp. 367–382.
-
35)
-
24. Zhang, Y., Zhang, Y., Zhang, J.: ‘CCR: clustering and collaborative representation for fast single image super-resolution’, IEEE Trans. Multimed., 2016, 18, (3), pp. 405–417.
-
36)
-
26. Wang, H., Gao, X., Zhang, B.: ‘Single image super-resolution using Gaussian process regression with dictionary-based sampling and student likelihood’, IEEE Trans. Image Process., 2017, 7, (26), pp. 3556–3558.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0153
Related content
content/journals/10.1049/iet-cvi.2017.0153
pub_keyword,iet_inspecKeyword,pub_concept
6
6