Dense Dual-Attention Network for Light Field Image Super-Resolution

• URL: http://arxiv.org/abs/2110.12114v1
• Date: Sat, 23 Oct 2021 02:10:47 GMT
• Title: Dense Dual-Attention Network for Light Field Image Super-Resolution
• Authors: Yu Mo, Yingqian Wang, Chao Xiao, Jungang Yang, Wei An
• Abstract summary: Light field (LF) images can be used to improve the performance of image super-resolution (SR) It is challenging to incorporate distinctive information from different views for LF image SR. We propose a dense dual-attention network for LF image SR.
• Score: 13.683743266136014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weakened as the depth of network increases. In this paper, we propose a dense dual-attention network for LF image SR. Specifically, we design a view attention module to adaptively capture discriminative features across different views and a channel attention module to selectively focus on informative information across all channels. These two modules are fed to two branches and stacked separately in a chain structure for adaptive fusion of hierarchical features and distillation of valid information. Meanwhile, a dense connection is used to fully exploit multi-level information. Extensive experiments demonstrate that our dense dual-attention mechanism can capture informative information across views and channels to improve SR performance. Comparative results show the advantage of our method over state-of-the-art methods on public datasets.

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