Computer Science ›› 2018, Vol. 45 ›› Issue (2): 147-151.doi: 10.11896/j.issn.1002-137X.2018.02.026

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Blind Single Image Super-resolution Using Maximizing Self-similarity Prior

LI Jian-hong, LV Ju-jian and WU Ya-rong   

  • Online:2018-02-15 Published:2018-11-13

Abstract: The relationship between the self-similarity property of image and image quality is close,and almost all the patches in the clear natural image have recurrence patches in itself or its lower scale.However,in the image which was processed by blur or noise,this appearance is not dramatically.Aiming at this phenomenon,this paper proposed a blind single image super-resolution algorithm using the prior of maximizing self-similarity.This algorithm estimates the high-resolution image and the blur kernel by iterative computation,thus making any patch in the final estimated high resolution image exists in the inputted low resolution image with maximizing probability.The proposed algorithm not only estimates the degradation kernel and the high-resolution image accurately,but also adapts the prior according to the inputted image to make the result more robust.Extensive experiments illustrate that our algorithm shows obvious advantages when comparing to other main stream algorithms in terms of PSNR and SSIM.

Key words: Single image super-resolution,Blind super-resolution,Self-similarity,Imaging model,Probability density function

[1] SUN J,SUN J,XU Z B,et al.Image super-resolution using gradient profile prior[C]∥CVPR.2008:1-8.
[2] SUN J,SUN J,XU Z B,et al.Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement[J].IEEE Transactions on Image Processing,2011,0(6):1529-1542.
[3] WANF Z W,LIU D,YANG J C,et al.Deep Networks for Image Super-Resolution with Sparse Prior[C]∥ICCV.2015:370-378.
[4] ROSTAMI M,WANG Z.Image Super-Resolution Based onSparsity Prior via Smoothed l0 Norm [C]∥2011 Symposium Advanced Intelligent Systems.Waterloo,ON,Canada,2011.
[5] DONG W S,ZHANG L,SHI G M.Centralized sparse representation for image restoration[C]∥ICCV.2011:1259-1266.
[6] ZHANG H C,YANG J C,ZHANG Y N,et al.Non-Local Kernel Regression for Image and Video Restoration[C]∥ECCV.2010:566-579.
[7] XU J,ZHANG L,ZUO W M,et al.Patch Group Based Nonlocal Self Similarity Prior Learning for Image Denoising[C]∥ICCV.2015:244-252.
[8] EFRAT N,GLASNER D,APARTSIN A,et al.Accurate BlurModels vs.Image Priors in Single Image Super-resolution[C]∥ICCV.2013:2832-2839.
[9] MUDENAGUDI U,SINGULAR R,KALRA P.Super Resolution Using Graph Cut[C]∥ACCV.2006:385-394.
[10] YANG J C,WRIGHT J,HUANG T S,et al.Image super-resolution via sparse representation[J].IEEE Transactions on Image Processing,2010,19(11):2861-2873.
[11] DONG C,CHEN C L,HE K M,et al.Learning a Deep Convolutional Network for Image Super-Resolution[C]∥ECCV.2014:184-199.
[12] GLASNER D,BAGON S,IRANI M.Super-resolution from a single image[C]∥ ICCV.2009:349-356.
[13] ZEYDE R,ELAD M,PROTTER M.On Single Image Scale-Up Using Sparse Representations[C]∥ICCS.2012:711-730.
[14] TIMOFTE R,DE V,GOOL L V.Anchored Neighborhood Regression for Fast Example-Based Super-Resolution[C]∥ICCV.2013:1920-1927.
[15] FATTAL R.Image upsampling via imposed edge statistics[C]∥ACM Transactions on Graphics.2007:95.
[16] JOSHI N,SZELISKI R,KRIEGMAN D J.PSF estimation using sharp edge prediction[C]∥CVPR.2008:1-8.
[17] MICHAELI T,IRANI M.Nonparametric Blind Super-resolution[C]∥ICCV.2013:945-952.
[18] KWON Y,KIM K I,TOMPKIN J,et al.Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,7(9):1792-1805.
[19] FERGUS R,SINGH B,HERTZMANN A,et al.Removing ca-mera shake from a single photograph[J].ACM Transactions on Graphics,2006,25(3):787-794.
[20] BAKER S,KANADE T.Limits on super-resolution and how to break them[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,4(9):1167-1183.
[21] ZONTAK M,MOSSERI I,IRANI M.Separating signal fromnoise using patch recurrence across scales[C]∥CVPR.2013:1195-1202.
[22] MICHAELI T,IRANI M.Blind Deblurring Using InternalPatch Recurrence [C]∥ECCV.2014:783-798.
[23] HARMELING S,SRA S,HIRSCH M,et al.Multiframe blind deconvolution,super-resolution,and saturation correction via incremental EM[C]∥ICIP.2010:3313-3316.
[24] LEVIN A,WEISS Y,DURAND F,et al.Understanding andevaluating blind deconvolution algorithms[C]∥CVPR.2009:1964-1971.
[25] ZORAN D,WEISS Y.From learning models of natural image patches to whole image restoration[C]∥ICCV.2011:479-486.
[26] WANG Y,YANG J,YIN W,et al.A New Alternating Minimization Algorithm for Total Variation Image Reconstruction[J].Siam Journal on Imaging Sciences,2008,1(3):248-272.
[27] KOMODAKIS N,PARAGIONS N.MRF-Based blind image de-convolution[J].Lecture Notes in Computer Science,2012,6:361-374.
[28] LI J H,WU Y R,LUO X N.Single Image Super-ResolutionUsing Maximizing Self-Similarity Prior[J].Mathematical Problems in Engineering,2016,2015(510):1-10.
[29] YANG J C,WRIGHT J,HUANG T,et al.Image super-resolution as sparse representation of raw image patches[C]∥CVPR.2008:1-8.
[30] TIMOFTE R,SMET V D,GOOL L V.A+:Adjusted Anchored Neighborhood Regression for Fast Super-Resolution[C]∥ACCV.2014:111-126.
[31] YANG M C,WANG Y C F.A Self-Learning Approach to Single Image Super-Resolution[J].IEEE Transactions on Multimedia,2013,5(3):498-508.

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