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

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 7; pp. 4431 - 4443
• Main Authors: Mo, Yu, Wang, Yingqian, Xiao, Chao, Yang, Jungang, An, Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; attention mechanism; Chain branching; Channels; Convolutional neural networks; Correlation; Data mining; dense connection; Distillation; Feature extraction; Image resolution; Light field; Modules; Performance enhancement; Spatial data; Spatial resolution; super-resolution; Superresolution
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2021.3121679

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.