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01.01.2021 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING

A Single Image Super-Resolution Algorithm Based on Dense Residual Convolutional Network

verfasst von: Liu Chengming, Duan Junyi, Pang Haibo

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 1/2021

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Abstract

Single image super-resolution via convolutional neural network (CNN) has demonstrated superior performance. In this paper, we propose a deep CNN model named super-resolution dense residual convolutional network (SRDCR) with the goal of reconstructing high quality high-resolution (HR) image. We propose a dense residual block (DRB) to learn residual information by residual connected layers. The local fusion layer (LFL) is then used to adaptively fuse the input of DRB and the output of the last residual layer. After multiple DRBs residual learning, the global fusion layer (GFL) reconstructs an HR image by adaptively combining the original low-resolution (LR) information and learned information. Experiments on extensive benchmark show that our method achieves favorable performance with much less CNN layers than DRB network.

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Titel: A Single Image Super-Resolution Algorithm Based on Dense Residual Convolutional Network
verfasst von: Liu Chengming
Duan Junyi
Pang Haibo
Publikationsdatum: 01.01.2021
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 1/2021
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661821010053

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