Simultaneous image fusion and denoising with adaptive sparse representation

IET Image Processing

Volumn 9, Issue 5, 2015, Pages 347-357

  Liu, Yu ^a   Wang, Zengfu ^a,b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE FUSION; IMAGE PROCESSING; SIGNAL PROCESSING;

COMPUTATIONAL COSTS; GRADIENT INFORMATIONS; HIGH QUALITY IMAGES; IMAGE PROCESSING APPLICATIONS; MULTI-MODAL IMAGE; OBJECTIVE ASSESSMENT; REDUNDANT DICTIONARIES; SPARSE REPRESENTATION;

IMAGE DENOISING;

EID: 84928618797     PISSN: 17519659     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-ipr.2014.0311     Document Type: Article

References (41)

1. Goshtasby, A.A., Nikolov, S.: 'Image fusion: advances in the state of the art', Inf. Fusion, 2007, 8, (2), pp. 114-118
2. Stathaki, T.: 'Image fusion: algorithms and applications' (Academic Press, London, UK, 2008)
3. Li, S., Kwok, J.,Wang, Y.: 'Combination of images with diverse focuses using the spatial frequency', Inf. Fusion, 2001, 2, (3), pp. 169-176
4. Aslantas, V., Kurban, R.: 'Fusion of multi-focus images using differential evolution algorithm', Expert Syst. Appl., 2010, 37, (12), pp. 8861-8870
5. Li, S., Yang, B.: 'Multifocus image fusion using region segmentation and spatial frequency', Image vis. Comput., 2008, 26, (7), pp. 971-979
6. Huang,W., Jing, Z.: 'Evaluation of focus measures in multi-focus image fusion', Pattern Recognit. Lett., 2007, 28, (4), pp. 493-500
7. Piella, G.: 'A general framework for multiresolution image fusion: from pixels to regions', Inf. Fusion, 2003, 4, (4), pp. 259-280
8. Toet, A.: 'A morphological pyramidal image decomposition', Pattern Recognit. Lett., 1989, 9, (4), pp. 255-261
9. Petrović, V.S., Xydeas, C.S.: 'Gradient-based multiresolution image fusion', IEEE Trans. Image Process., 2004, 13, (2), pp. 228-237
10. Li, H., Manjunath, B.S., Mitra, S.K.: 'Multisensor image fusion using the wavelet transform', Graph. Models Image Process., 1995, 57, (3), pp. 235-245
11. Lewis, J.J., O'Callaghan, R.J., Nikolov, S.G., et al.: 'Pixel- and region-based image fusion with complex wavelets', Inf. Fusion, 2007, 8, (2), pp. 119-130
12. Nencini, F., Garzelli, A., Baronti, S., et al.: 'Remote sensing image fusion using the curvelet transform', Inf. Fusion, 2007, 8, (2), pp. 143-156
13. Zhang, Q., Guo, B.: 'Multifocus image fusion using the nonsubsampled contourlet transform', Signal Process., 2009, 89, (7), pp. 1334-1346
14. Gao, G., Xu, L., Feng, D.: 'Multi-focus image fusion based on non-subsampled shearlet transform', IET Image Process., 2013, 7, (6), pp. 633-639
15. Jiang, Y., Wang, M.: 'Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter', IET Image Process., 2014, 8, (3), pp. 183-190
16. Zhao, H., Shang, Z., Tang, Y., et al.: 'Multi-focus image fusion based on the neighbor distance', Pattern Recognit., 2013, 46, (3), pp. 1002-1011
17. Elad, M., Aharon, M.: 'Image denoising via sparse and redundant representations over learned dictionaries', IEEE Trans. Image Process., 2006, 15, (2), pp. 3736-3745
18. Yang, J.,Wright, J., Huang, T., et al.: 'Image super-resolution via sparse representation', IEEE Trans. Image Process., 2010, 19, (11), pp. 2861-2873
19. Dong, W., Zhang, L., Shi, G., et al.: 'Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization', IEEE Trans. Image Process., 2011, 20, (7), pp. 1838-1857
20. Yang, B., Li, S.: 'Multifocus image fusion and restoration with sparse representation', IEEE Trans. Instrum. Meas., 2010, 59, (4), pp. 884-892
21. Yang, B., Li, S.: 'Pixel-level image fusion with simultaneous orthogonal matching pursuit', Inf. Fusion, 2012, 13, (1), pp. 10-19
22. Yu, N., Qiu, T., Bi, F., et al.: 'Image features extraction and fusion based on joint sparse representation', IEEE J. Sel. Top. Signal Process., 2011, 5, (5), pp. 1074-1082
23. Yin, H., Li, S., Fang, L.: 'Simultaneous image fusion and super-resolution using sparse representation', Inf. Fusion, 2013, 14, (3), pp. 229-240
24. Iqbal, M., Chen, J.: 'Unification of image fusion and super-resolution using jointly trained dictionaries and local information contents', IET Image Process., 2012, 6, (9), pp. 1299-1310
25. Liu, Y., Wang, Z.: 'Multi-focus image fusion based on sparse representation with adaptive sparse domain selection'. Proc. Int. Conf. Image Graphics, Qingdao, China, July 2013, pp. 591-596
26. Elad, M., Yavneh, I.: 'A plurality of sparse representations is better than the sparsest one alone', IEEE Trans. Inf. Theory, 2009, 55, (10), pp. 4701-4714
27. Olshausen, B., Field, J.: 'Emergence of simple-cell receptive field properties by learning a sparse code for natural images', Nature, 1996, 381, (6583), pp. 607-609
28. Mallat, S., Zhang, Z.: 'Matching pursuits with time-frequency dictionaries', IEEE Trans. Signal Process., 1993, 41, (12), pp. 3397-3415
29. Aharon, M., Elad, M., Bruckstein, A.: 'K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation', IEEE Trans. Signal Process., 2006, 54, (11), pp. 4311-4322
30. Mairal, J., Elad, M., Sapiro, G.: 'Sparse representation for color image restoration', IEEE Trans. Image Process., 2008, 17, (1), pp. 53-69
31. Lowe, D.: 'Distinctive image features from scale-invariant keypoints', Int. J. Comput. Vis., 2004, 60, (2), pp. 91-110
32. Li, S., Yang, B., Hu, J.: 'Performance comparison of different multi-resolution transforms for image fusion', Inf. Fusion, 2011, 12, (2), pp. 74-84
33. Starck, J., Candès, E., Donoho, D.: 'The curvelet transform for image denoising', IEEE Trans. Image Process., 2002, 11, (6), pp. 670-684
34. da Cunha, A.L., Zhou, J., Do, M.N.: 'The nonsubsampled contourlet transform: theory, design, and applications', IEEE Trans. Image Process., 2006, 15, (10), pp. 3089-3101
35. Liu, Z., Blasch, E., Xue, Z., et al.: 'Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study', IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (1), pp. 94-109
36. Hossny, M., Nahavandi, S., Creighton, D.: 'Comments on 'information measure for performance of image fusion', Electron. Lett., 2008, 44, (18), pp. 1066-1067
37. Qu, G., Zhang, D., Yan, P.: 'Information measure for performance of image fusion', Electron. Lett., 2002, 38, (7), pp. 313-315
38. Xydeas, C., Petrović, V.: 'Objective image fusion performance measure', Electron. Lett., 2000, 36, (4), pp. 308-309
39. Yang, C., Zhang, J., Wang, X., et al.: 'A novel similarity based quality metric for image fusion', Inf. Fusion, 2008, 9, (2), pp. 156-160
40. Wang, Z., Bovik, A., Sheikh, H., et al.: 'Image quality assessment: from error visibility to structural similarity', IEEE Trans. Image Process., 2004, 13, (4), pp. 600-612
41. Chen, Y., Blum, R.: 'A new automated quality assessment algorithm for image fusion', Image Vis. Comput., 2009, 27, (10), pp. 1421-1432