Detail-Preserving Transformer for Light Field Image Super-Resolution

• URL: http://arxiv.org/abs/2201.00346v1
• Date: Sun, 2 Jan 2022 12:33:23 GMT
• Title: Detail-Preserving Transformer for Light Field Image Super-Resolution
• Authors: Shunzhou Wang, Tianfei Zhou, Yao Lu, Huijun Di
• Abstract summary: We put forth a novel formulation built upon Transformers, by treating light field super-resolution as a sequence-to-sequence reconstruction task. We propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence.
• Score: 15.53525700552796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, numerous algorithms have been developed to tackle the problem of light field super-resolution (LFSR), i.e., super-resolving low-resolution light fields to gain high-resolution views. Despite delivering encouraging results, these approaches are all convolution-based, and are naturally weak in global relation modeling of sub-aperture images necessarily to characterize the inherent structure of light fields. In this paper, we put forth a novel formulation built upon Transformers, by treating LFSR as a sequence-to-sequence reconstruction task. In particular, our model regards sub-aperture images of each vertical or horizontal angular view as a sequence, and establishes long-range geometric dependencies within each sequence via a spatial-angular locally-enhanced self-attention layer, which maintains the locality of each sub-aperture image as well. Additionally, to better recover image details, we propose a detail-preserving Transformer (termed as DPT), by leveraging gradient maps of light field to guide the sequence learning. DPT consists of two branches, with each associated with a Transformer for learning from an original or gradient image sequence. The two branches are finally fused to obtain comprehensive feature representations for reconstruction. Evaluations are conducted on a number of light field datasets, including real-world scenes and synthetic data. The proposed method achieves superior performance comparing with other state-of-the-art schemes. Our code is publicly available at: https://github.com/BITszwang/DPT.

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