SSIM-inspired image restoration using sparse representation

Eurasip Journal on Advances in Signal Processing

Volumn 2012, Issue 1, 2012, Pages

  Rehman, Abdul ^a   Rostami, Mohammad ^a   Wang, Zhou ^a   Brunet, Dominique ^a   Vrscay, Edward R ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT DESCENT ALGORITHMS; IMAGE MEASURE; IMAGE RESTORATION PROBLEM; INDEX VALUES; MEAN SQUARED ERROR; OPTIMAL PERFORMANCE; OPTIMIZATION PROBLEMS; POOR PERFORMANCE; RECONSTRUCTED IMAGE; SPARSE REPRESENTATION; SPARSE SIGNAL REPRESENTATION; STRUCTURAL SIMILARITY; SUPERRESOLUTION METHODS; VISUAL QUALITIES;

ALGORITHMS; IMAGE RECONSTRUCTION; OPTIMIZATION;

LEAST SQUARES APPROXIMATIONS;

EID: 84872902917     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1186/1687-6180-2012-16     Document Type: Article

References (43)

1. K Dabov, A Foi, V Katkovnik, K Egiazarian, Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080-2095 (2007) (Pubitemid 47225460)
2. A Buades, B Coll, JM Morel, A review of image denoising algorithms, with a new one. Multi-scale Model Simul. 4(2), 490-530 (2005). doi:10.1137/040616024 (Pubitemid 43890582)
3. M Elad, M Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process. 15(12), 3736-3745 (2006) (Pubitemid 44811686)
4. H Hou, H Andrews, Cubic splines for image interpolation and digital filtering. IEEE Trans Signal Process. 26, 508-517 (1978). doi:10.1109/TASSP. 1978.1163154 (Pubitemid 9437593)
5. J Yang, J Wright, T Huang, Y Ma, Image super-resolution via sparse representation. IEEE Trans Image Process. 19(11), 2861-2873 (2010)
6. J Yang, J Wright, TS Huang, Y Ma, Image super-resolution as sparse representation of raw image patches. in Proc IEEE Comput Vis Pattern Recognit 1-8 (2008)
7. Z Wang, AC Bovik, Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag. 26, 98-117 (2009)
8. Z Wang, AC Bovik, HR Sheikh, EP Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 13(4), 600-612 (2004). doi:10.1109/TIP.2003.819861
9. Joint Video Team (JVT) Reference Software [Online], http://iphome.hhi.de/ suehring/tml/download/old-jm
10. Y Gao, A Rehman, Z Wang, CW-SSIM Based image classification, in IEEE International Conference on Image Processing ICIP, Brussels, Belgium, pp. 1249-1252 (2011)
11. G Piella, H Heijmans, A new quality metric for image fusion, in IEEE International Conference on Image Processing (ICIP), vol. 3. Barcelona, Spain, pp. 173-176 (2003)
12. D Brunet, ER Vrscay, Z Wang, in On the Mathematical Properties of the Structural Similarity Index (Preprint), University of Waterloo, Waterloo, 2011 http://www.math.uwaterloo.ca/~dbrunet
13. SS Channappayya, AC Bovik, C Caramanis, R Heath, Design of linear equalizers optimized for the structural similarity index. IEEE Trans Image Process. 17(6), 857-872 (2008) (Pubitemid 351772605)
14. Z Wang, Q Li, X Shang, Perceptual image coding based on a maximum of minimal structural similarity criterion. IEEE Int Conf Image Process. 2, II-121-II-124 (2007)
15. A Rehman, Z Wang, SSIM-based non-local means image denoising, in IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, pp. 1-4 (2011)
16. S Wang, A Rehman, Z Wang, S Ma, W Gao, Rate-SSIM optimization for video coding, in IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 11), Prague, Czech Republic, pp. 833-836 (22-27 May 2011)
17. T Ou, Y Huang, H Chen, A perceptual-based approach to bit allocation for H.264 encoder, in SPIE Visual Communications and Image Processing, pp. 77441B (11 July 2010)
18. Z Mai, C Yang, K Kuang, L Po, A novel motion estimation method based on structural similarity for h.264 inter prediction, in IEEE Int Conf Acoust Speech Signal Process, vol. 2. (Toulouse, 2006), pp. 913-916
19. C Yang, H Wang, L Po, Improved inter prediction based on structural similarity in H.264, in IEEE Int Conf Signal Process Commun, vol. 2. Dubai, pp. 340-343 (24-27 Nov. 2007)
20. R Zeyde, M Elad, M Protter, On single image scale-up using sparserepresentations, in Curves & Surfaces, Avignon-France, pp. 711-730 (24-30 June 2010)
21. A Savitzky, MJE Golay, Smoothing and differentiation of data by simplified least squares procedures. Anal Chem. 36, 1627-1639 (1964). doi:10.1021/ac60214a047
22. AN Tikhonov, VY Arsenin, Solutions of Ill-Posed Problem (V. H. Winston, Washington DC, 1977)
23. LI Rudin, S Osher, E Fatemi, Nonlinear total variation based noise removal algorithms. Physica D. 60, 259-268 (1992). doi:10.1016/0167-2789 (92) 90242-F
24. M Protter, M Elad, Image sequence denoising via sparse and redundant representations. IEEE Trans Image Process. 18, 27-35 (2009)
25. J Mairal, G Sapiro, M Elad, Learning multiscale sparse representations for image and video restoration. Multiscale Model Simul. 7, 214-241 (2008). doi:10.1137/070697653
26. EJ Candés, J Romberg, T Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory. 52(2), 489-509 (2006) (Pubitemid 43174093)
27. DL Donoho, Compressed sensing. IEEE Trans Inf Theory. 52(4), 1289-1306 (2006)
28. M Aharon, M Elad, A Bruckstein, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process. 54(11), 4311-4322 (2006) (Pubitemid 44637761)
29. Y Pati, R Rezaiifar, P Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in Twenty Seventh Asilomar Conference on Signals, Systems and Computers, vol. 1. Pacific Grove, CA, pp. 40-44 (Nov. 1993)
30. D Brunet, ER Vrscay, Z Wang, Structural similarity-based approximation of signals and images using orthogonal bases, in Proc Int Conf on Image Analysis and Recognition, ed. by M Kamel, A Campilho (Springer, Heidelberg, 2010), pp. 11-22. vol. 6111 of LNCS
31. S Wang, A Rehman, Z Wang, S Ma, W Gao, SSIM-inspired divisive normalization for perceptual video coding, in IEEE International Conference on Image Processing ICIP, Brussels, Belgium, pp. 1657-1660 (11-14 Sept 2011)
32. MJ Wainwright, EP Simoncelli, Scale mixtures of gaussians and the statistics of natural images. Adv Neural Inf Process Syst. 12, 855-861 (2000)
33. S Lyu, EP Simoncelli, Statistically and perceptually motivated nonlinear image representation, in Proc SPIE Conf Human Vision Electron Imaging XII, vol. 6492. San Jose, CA, pp. 649207-1-649207-15 (2007)
34. J Foley, Human luminance pattern mechanisms: masking experiments require a new model. J Opt Soc Am. 11, 1710-1719 (1994). doi:10.1364/JOSAA.11.001710
35. AB Watson, JA Solomon, Model of visual contrast gain control and pattern masking. J Opt Soc Am. 14, 2379-2391 (1997). doi:10.1364/JOSAA.14.002379 (Pubitemid 127567280)
36. DJ Heeger, Normalization of cell responses in cat striate cortex. Vis Neural Sci. 9, 181-198 (1992)
37. EP Simoncelli, DJ Heeger, A model of neuronal responses in visual area MT. Vis Res. 38, 743-761 (1998). doi:10.1016/S0042-6989 (97) 00183-1 (Pubitemid 28081676)
38. Q Li, Z Wang, Reduced-reference image quality assessment using divisive normalization-based image representation. IEEE J Coupled dictionary training for image s Spec Top Signal Process. 3, 202-211 (2009)
39. A Rehman, Z Wang, Reduced-reference SSIM estimation, in International Conference on Image Processing, Hong Kong, China, pp. 289-292 (27-29 Sept 2010)
40. J Malo, I Epifanio, R Navarro, EP Simoncelli, Non-linear image representation for efficient perceptual coding. IEEE Trans Image Process. 15, 68-80 (2006) (Pubitemid 43051456)
41. J Portilla, V Strela, MJ Wainwright, EP Simoncelli, Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans Image Process. 12, 1338-1351 (2003). doi:10.1109/TIP.2003.818640
42. Z Wang, EP Simoncelli, Maximum differentiation (MAD) competition: a methodology for comparing computational models of perceptual quantities. J Vis. 8(12), 1-13 (2008). doi:10.1167/8.12.1
43. J Yang, Z Wang, Z Lin, T Huang, Coupled dictionary training for image super-resolution. http://www.ifp.illinois.edu/~jyang29/ (2011)