广义非局部均值和自相似性的超分辨率算法

doi:10.3969/j.issn.1001-2400.2014.04.018

Abstract

Abstract:

A super-resolution method based on generalized nonlocal mean and self-similarity is proposed. The proposed method not only adopts the self-similarity of the image by taking the low-resolution image and its downsampled version as a training set but uses the nonlocal mean algorithm to improve the quality of the restored image. The proposed method first extracts the features of the low image by using the difference of Gaussians, and then a generalized nonlocal mean algorithm is adopted to estimate the high-frequency details of the low image. Experimental results show that the proposed algorithm has a good performance, and that the high-resolution image generated by the proposed method is of better subjective and objective quality compared with other methods.

Key words: image restoration, image processing, learning-based super-resolution, nonlocal means

CLC Number:

TP391.4

WU Wei;ZHENG Chenglin;ZHANG Yingying;ZHOU Shouhuan. Image super-resolution using generalized nonlocal mean and self-similarity[J].J4, 2014, 41(4): 100-107.

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Image super-resolution using generalized nonlocal mean and self-similarity

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