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2015 | OriginalPaper | Chapter

Single-Frame Super-Resolution via Compressive Sampling on Hybrid Reconstructions

Authors : Ji-Ping Zhang, Tao Dai, Shu-Tao Xia

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

It is well known that super-resolution (SR) is a difficult problem, especially the single-frame super-resolution (SFSR). In this paper, we propose a novel SFSR method, called compressive sampling on hybrid reconstructions (CSHR), with high reconstruction quality and relatively low computation cost. It mainly depends on the combination of the results of other SR methods, which are characteristic of high speed and low quality SR results alone. As a result, CSHR inherits the merit of low computation cost. We resample those low quality SR results in DCT domain instead of in pixel domain and regard the similar expansion coefficients as consensus which would be compressively sampled later. In CSHR, obtaining a high resolution image is only to solve a convex optimization program. We use compressed sensing theory to ensure the efficiency of our method. Also, we give some theoretic results. Experimental results show the effectiveness of the proposed method when compared to some state-of-the-art methods.

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Metadata
Title
Single-Frame Super-Resolution via Compressive Sampling on Hybrid Reconstructions
Authors
Ji-Ping Zhang
Tao Dai
Shu-Tao Xia
Copyright Year
2015
DOI
https://doi.org/10.1007/978-3-319-26555-1_69

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