Exploiting Spatial and Angular Correlations With Deep Efficient Transformers for Light Field Image Super-Resolution

Global context information is particularly important for comprehensive scene understanding. It helps clarify local confusions and smooth predictions to achieve fine-grained and coherent results. However, most existing light field processing methods leverage convolution layers to model spatial and angular information. The limited receptive field restricts them to learn long-range dependency in LF structure. In this paper, we propose a novel network based on deep efficient transformers ( i.e., LF-DET) for LF spatial super-resolution. It develops a spatial-angular separable transformer encoder with two modeling strategies termed as sub-sampling spatial modeling and multi-scale angular modeling for global context interaction. Specifically, the former utilizes a sub-sampling convolution layer to alleviate the problem of huge computational cost when capturing spatial information within each sub-aperture image. In this way, our model can cascade more transformers to continuously enhance feature representation with limited resources. The latter processes multi-scale macro-pixel regions to extract and aggregate angular features focusing on different disparity ranges to well adapt to disparity variations. Besides, we capture strong similarities among surrounding pixels by dynamic positional encodings to fill the gap of transformers that lack of local information interaction. The experimental results on both real-world and synthetic LF datasets confirm our LF-DET achieves a significant performance improvement compared with state-of-the-art methods. Furthermore, our LF-DET shows high robustness to disparity variations through the proposed multi-scale angular modeling.