Dense Dual-Attention Network for Light Field Image Super-Resolution
This work addresses a domain-specific problem for computer vision researchers and practitioners working with light field images, offering incremental improvements in super-resolution.
The paper tackles the problem of light field image super-resolution by incorporating angular and spatial information from different views, proposing a dense dual-attention network that improves performance over state-of-the-art methods on public datasets.
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.