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2020 | OriginalPaper | Chapter

A Novel Attention Enhanced Dense Network for Image Super-Resolution

Authors : Zhong-Han Niu, Yang-Hao Zhou, Yu-Bin Yang, Jian-Cong Fan

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

Deep convolutional neural networks (CNNs) have recently achieved impressive performance in image super-resolution (SR). However, they usually treat the spatial features and channel-wise features indiscriminatingly and fail to take full advantage of hierarchical features, restricting adaptive ability. To address these issues, we propose a novel attention enhanced dense network (AEDN) to adaptively recalibrate each kernel and feature for different inputs, by integrating both spatial attention (SA) and channel attention (CA) modules in the proposed network. In experiments, we explore the effect of attention mechanism and present quantitative and qualitative evaluations, where the results show that the proposed AEDN outperforms state-of-the-art methods by effectively suppressing the artifacts and faithfully recovering more high-frequency image details.

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Metadata
Title
A Novel Attention Enhanced Dense Network for Image Super-Resolution
Authors
Zhong-Han Niu
Yang-Hao Zhou
Yu-Bin Yang
Jian-Cong Fan
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-37731-1_46