Title: Image super-resolution via Gaussian scale patch group sparse representation

Authors: Wu Minghu; Lu Yaqi; Zhao Nan; Liu Min; Liu Cong; Zhou Shangli

Addresses: Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China ' Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy, Storage System, Hubei University of Technology, Wuhan, 430068, China ' Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China ' Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China ' Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, 430068, China ' Hubei Power Grid Intelligent Control and Equipment Engineering Technology Research Center, Hubei University of Technology, Wuhan, 430068, China

Abstract: This passage puts forward a Gaussian scale patch group sparse representation method, to solve the shortage problem of traditional image super-resolution restoration schemes. Our image reconstruction method is focused on the optimisation of sparse representation method model, which brings the method and performance improvement to image sparse reconstruction. The overall framework of our approach is as follows. First of all, we utilised the nonlocal similar patches to extract the patch groups, and then using the simultaneous sparse coding to develop a nonlocal extension of Gaussian scale mixture model. In the end, we integrate the patch group model and Gaussian scale sparsity model into encoding framework. The experimental simulation results show that the proposed framework method can both maintain the clarity of the edge and also inhibit the bad artefacts. Our method often provides a higher subjective/objective quality of reconstructed images than other competing methods.

Keywords: sparse representation; image super-resolution; patch grouping; Gaussian scale mixture.

DOI: 10.1504/IJIIDS.2018.092002

International Journal of Intelligent Information and Database Systems, 2018 Vol.11 No.2/3, pp.186 - 206

Received: 06 Sep 2017
Accepted: 19 Nov 2017
Published online: 24 May 2018 *

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