RAISR: Rapid and Accurate Image Super Resolution

IEEE Transactions on Computational Imaging

Volumn 3, Issue 1, 2017, Pages 110-125

  Romano, Yaniv ^a,b   Isidoro, John ^a   Milanfar, Peyman ^a  

^a GOOGLE INC   (United States)

^b TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

Author keywords

Filter Learning; image enhancement; image sharpening; super resolution

Indexed keywords

OPTICAL RESOLVING POWER;

CONTRAST ENHANCEMENT; FILTER LEARNING; HIGH RESOLUTION IMAGE; IMAGE SHARPENING; IMAGE SUPER RESOLUTIONS; NOISE AMPLIFICATION; ORDERS OF MAGNITUDE; SUPER RESOLUTION;

IMAGE ENHANCEMENT;

EID: 85040556111     PISSN: None     EISSN: 23339403     Source Type: Journal    
DOI: 10.1109/TCI.2016.2629284     Document Type: Article

References (51)

1. R. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans. Acoust., Speech, Signal Process. vol. 29, no. 6, pp. 1153-1160, Dec. 1981.
2. H. Hou and H. Andrews, "Cubic splines for image interpolation and digital filtering," IEEE Trans. Acoust., Speech, Signal Process. vol. 26, no. 6, pp. 508-517, Dec. 1978.
3. A. Buades, B. Coll, and J. M. Morel, "A review of image denoising algorithms, with a new one," Multiscale Model. Simul. vol. 4, no. 2, pp. 490-530, 2005.
4. H. Takeda, S. Farsiu, and P. Milanfar, "Kernel regression for image processing and reconstruction," IEEE Trans. Image Process. vol. 16, no. 2, pp. 349-366, Feb. 2007.
5. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Trans. Image Process. vol. 16, no. 8, pp. 2080-2095, Aug. 2007.
6. H. Takeda, P. Milanfar, M. Protter, and M. Elad, "Super-resolutionwithout explicit subpixelmotion estimation," IEEE Trans. Image Process. vol. 18, no. 9, pp. 1958-1975, Sep. 2009.
7. M. Aharon, M. Elad, and A. Bruckstein, "The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process. vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
8. J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution as sparse representation of raw image patches," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Jun. 2008, pp. 1-8.
9. R. Zeyde, M. Elad, and M. Protter, "On single image scale-up using sparse representations," in Curves and Surfaces. New York, NY, USA: Springer, 2012, pp. 711-730.
10. R. Timofte, V. De, and L. Van Gool, "Anchored neighborhood regression for fast example-based super-resolution," in Proc. IEEE Int. Conf. Comput. Vis. Dec. 2013, pp. 1920-1927.
11. R. Timofte, V. De Smet, and L. Van Gool, "A+: Adjusted anchored neighborhood regression for fast super-resolution," in Proc. Asian Conf. Comput. Vis. 2014, pp. 111-126.
12. Y. Romano, M. Protter, and M. Elad, "Single image interpolation via adaptive nonlocal sparsity-based modeling," IEEE Trans. Image Process. vol. 23, no. 7, pp. 3085-3098, Jul. 2014.
13. D. Zoran and Y. Weiss, "From learning models of natural image patches to whole image restoration," in Proc. IEEE Int. Conf. Comput. Vis. 2011, pp. 479-486.
14. J. Yang, Z. Wang, Z. Lin, S. Cohen, and T. Huang, "Coupled dictionary training for image super-resolution," IEEE Trans. Image Process. vol. 21, no. 8, pp. 3467-3478, Aug. 2012.
15. T. Peleg and M. Elad, "A statistical prediction model based on sparse representations for single image super-resolution," IEEE Trans. Image Process. vol. 23, no. 6, pp. 2569-2582, Jun. 2014.
16. C. Dong, C. C. Loy, K. He, and X. Tang, "Learning a deep convolutional network for image super-resolution," in Proc. Eur. Conf. Comput. Vis. 2014, pp. 184-199.
17. D. Chao, C. C. Loy, H. Kaiming, and T. Xiaoou, "Image super-resolution using deep convolutional networks," IEEE Trans. Pattern Anal. Mach. Intell. vol. 38, no. 2, pp. 295-307, Feb. 2016.
18. D. Dai, R. Timofte, and L. Van Gool, "Jointly optimized regressors for image super-resolution," in Proc. Comput. Graph. Forum vol. 34, no. 2, pp. 95-104, 2015.
19. Y. LeCun et al. "Backpropagation applied to handwritten zip code recognition," Neural Comput. vol. 1, no. 4, pp. 541-551, 1989.
20. R. Zabih and J. Woodfill, "Non-parametric local transforms for computing visual correspondence," in Proc. Eur. Conf. Comput. Vision 1994, pp. 151-158.
21. A. Polesel, G. Ramponi, and V. J. Mathews, "Image enhancement via adaptive unsharp masking," IEEE Trans. Image Process. vol. 9, no. 3, pp. 505-510, 2000.
22. D. Marr and E. Hildreth, "Theory of edge detection," Proc. R. Soc. London B, Biol. Sci. vol. 207, no. 1167, pp. 187-217, 1980.
23. H. Winnemöller, J. E. Kyprianidis, and S. C. Olsen, "XDoG: An extended difference-of-Gaussians compendium including advanced image stylization," Comput. Graph. vol. 36, no. 6, pp. 740-753, 2012.
24. B. Zhang and J. P. Allebach, "Adaptive bilateral filter for sharpness enhancement and noise removal," IEEE Trans. Image Process. vol. 17, no. 5, pp. 664-678, May 2008.
25. K. He, J. Sun, and X. Tang, "Guided image filtering," in Proc. Eur. Conf. Comput. Vision 2010, pp. 1-14.
26. H. Talebi and P. Milanfar, "Nonlocal image editing," IEEE Trans. Image Process. vol. 23, no. 10, pp. 4460-4473, Oct. 2014.
27. A. Kheradmand and P. Milanfar, "Nonlinear structure-aware image sharpeningwith difference of smoothing operators," Frontiers ICT vol. 2, 2015, Art. no. 22.
28. X. Liu, G. Cheung, and X. Wu, "Joint denoising and contrast enhancement of images using graph laplacian operator," in Proc. IEEEInt. Conf. Acoust., Speech, Signal Process. 2015, pp. 2274-2278.
29. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Proc. IEEE Int. Conf. Comput. Vis. Jan. 1998, pp. 836-846.
30. F. Durand and J. Dorsey, "Fast bilateral filtering for the display of high=dynamic-range images," in Proc. ACM Trans. Graph. vol. 21, no. 3, 2002, pp. 257-266.
31. R. Barrett et al. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods Philadelphia, PA, USA: SIAM, 1994.
32. S.-C. Jeong and B. C. Song, "Training-based super-resolution algorithm using k-means clustering and detail enhancement," in Proc. Eur. Signal Process. Conf. 2010, pp. 1791-1795.
33. G. Yu, G. Sapiro, and S. Mallat, "Solving inverse problems with piecewise linear estimators: From Gaussian mixture models to structured sparsity," IEEE Trans. Image Process. vol. 21, no. 5, pp. 2481-2499, May 2012.
34. V. Papyan and M. Elad, "Multi-scale patch-based image restoration," IEEE Trans. Image Process. vol. 25, no. 1, pp. 249-261, Jan. 2016.
35. X. Feng and P. Milanfar, "Multiscale principal components analysis for image local orientation estimation," in Proc. 36th AsilomarConf. Signals, Syst. Comput. Nov. 2002, pp. 478-482.
36. M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, "Lowcomplexity single-image super-resolution based on nonnegative neighbor embedding," in Proc. Brit. Mach. Vis. Conf. 2012, pp. 1-10.
37. H. Chang, D.-Y. Yeung, and Y. Xiong, "Super-resolution through neighbor embedding," in Proc. Conf. Comput. Vis. Pattern Recognit. 2004, http://ieeexplore.ieee.org/document/1315043/
38. D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proc. IEEE Int. Conf. Comput. Vis. 2001, vol. 2, pp. 416-423.
39. H. Talebi, X. Zhu, and P. Milanfar, "How to SAIF-ly boost denoising performance," IEEE Trans. Image Process. vol. 22, no. 4, pp. 1470-1485, Apr. 2013.
40. P. Milanfar, "A tour of modern image filtering: New insights and methods, both practical and theoretical," IEEE Signal Process. Mag. vol. 30, no. 1, pp. 106-128, Jan. 2013.
41. Y. Romano and M. Elad, "Improving K-SVD denoising by post-processing its method-noise," in Proc. IEEE Int. Conf. Image Process. 2013, pp. 435-439.
42. H. Talebi and P. Milanfar, "Global image denoising," IEEE Trans. Image Process. vol. 23, no. 2, pp. 755-768, Feb. 2014.
43. A. Kheradmand and P. Milanfar, "A general framework for regularized, similarity-based image restoration," IEEE Trans. Image Process. vol. 23, no. 12, pp. 5136-5151, Dec. 2014.
44. Y. Romano and M. Elad, "Patch-disagreement as away to improve K-SVD denoising," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. 2015, pp. 1280-1284.
45. Y. Romano and M. Elad, "Boosting of image denoising algorithms," SIAM J. Imag. Sci. vol. 8, no. 2, pp. 1187-1219, 2015.
46. G. Ghimpeteanu, T. Batard, M. Bertalmio, and S. Levine, "A decomposition framework for image denoising algorithms," IEEE Trans. Image Process. vol. 25, no. 1, pp. 388-399, Jan. 2016.
47. S. C. Park, M. K. Park, and M. G. Kang, "Super-resolution image reconstruction: A technical overview," IEEE Signal Process. Mag. vol. 20, no. 3, pp. 21-36, May 2003.
48. W. Dong, L. Zhang, G. Shi, and X. Wu, "Nonlocal back-projection for adaptive image enlargement," in Proc. IEEE Int. Conf. Image Process. 2009, pp. 349-352.
49. L. He, H. Qi, and R. Zaretzki, "Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2013, pp. 345-352.
50. R. R. Makwana and N. D. Mehta, "Single image super-resolution via iterative back projection based Canny edge detection and a Gabor filter prior," Int. J. Soft Comput. Eng. vol. 3, pp. 2231-2307, 2013.
51. M. Irani and S. Peleg, "Improving resolution by image registration," CVGIP: Graph. Models Image Process. vol. 53, no. 3, pp. 231-239, 1991.