Light Field Image Super-Resolution with Transformers
Abstract
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 https://github.com/ZhengyuLiang24/LFT.
Cite
@article{arxiv.2108.07597,
title = {Light Field Image Super-Resolution with Transformers},
author = {Zhengyu Liang and Yingqian Wang and Longguang Wang and Jungang Yang and Shilin Zhou},
journal= {arXiv preprint arXiv:2108.07597},
year = {2022}
}
Comments
This paper has been accepted by IEEE Signal Processing Letters. The current version on arXiv is identical to the final accepted version in content, but integrates the supplemental material (i.e., related work and visual comparisons) to the main body of the paper. Moreover, figures and tables of the arxiv version were zoomed for better visualization