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Piecewise linear regression-based single image super-resolution via Hadamard transform

Luo, Jingjing, Sun, Xianfang, Yiu, Man Lung, Jin, Longcun and Peng, Xinyi 2018. Piecewise linear regression-based single image super-resolution via Hadamard transform. Information Sciences 462 , pp. 315-330. 10.1016/j.ins.2018.06.030

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Abstract

Image super-resolution (SR) has extensive applications in surveillance systems, satellite imaging, medical imaging, and ultra-high definition display devices. The state-ofthe-art methods for SR still incur considerable running time. In this paper, we propose a novel approach based on Hadamard pattern and tree search structure in order to reduce the running time significantly. In this approach, LR (low-resolution)-HR (high-resolution) training patch pairs are classified into different classes based on the Hadamard patterns generated from the LR training patches. The mapping relationship between the LR space and the HR space for each class is then learned and used for SR. Experimental results show that the proposed method can achieve comparable accuracy as state-of-the-art methods with much faster running speed.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Elsevier
ISSN: 0020-0255
Date of First Compliant Deposit: 18 June 2018
Date of Acceptance: 12 June 2018
Last Modified: 12 Mar 2020 21:03
URI: http://orca.cf.ac.uk/id/eprint/112246

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  • Piecewise linear regression-based single image super-resolution via Hadamard transform. (deposited 18 Jun 2018 14:30) [Currently Displayed]

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