Light Field Image Super-Resolution With Transformers

Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remarkable performance in LF image SR, these methods cannot fully model the non-local properties of the 4D LF data. In this p...

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Bibliographic Details
Published in:IEEE signal processing letters 2022, Vol.29, p.563-567
Main Authors: Liang, Zhengyu, Wang, Yingqian, Wang, Longguang, Yang, Jungang, Zhou, Shilin
Format: Article
Language:English
Subjects:
Ablation
Aperture imaging
Convolution
Encoding
Feature extraction
Image reconstruction
Image resolution
image super-resolution
Light field
Light fields
Spatial resolution
Superresolution
transformer
Transformers
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Summary:Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remarkable performance in LF image SR, these methods cannot fully model the non-local properties of the 4D LF data. In this paper, we propose a simple but effective Transformer-based method for LF image SR. In our method, an angular Transformer is designed to incorporate complementary information among different views, and a spatial Transformer is developed to capture both local and long-range dependencies within each sub-aperture image. With the proposed angular and spatial Transformers, the beneficial information in an LF can be fully exploited and the SR performance is boosted. We validate the effectiveness of our angular and spatial Transformers through extensive ablation studies, and compare our method to recent state-of-the-art methods on five public LF datasets. Our method achieves superior SR performance with a small model size and low computational cost. Code is available at. 1
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3146798