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

Abstract

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

LI Jian-hong, LV Ju-jian and WU Ya-rong. Blind Single Image Super-resolution Using Maximizing Self-similarity Prior[J].Computer Science, 2018, 45(2): 147-151.

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