Hand-held light field cameras suffer from low spatial resolution due to the inherent spatio-angular tradeoff. In this paper, we propose a method to improve the spatial resolution of light field images with the aid of the Texture Transformer Network (TTSR). The proposed method consists of three modules: the first module produces an all-in focus high-resolution perspective image which serves as a reference image for the second module, i.e. TTSR, which in turn produces a high-resolution light field. The last module refines the spatial resolution by imposing a light field prior. The results demonstrate around 4 dB to 6 dB PSNR gain over a bicubically resized light field image
@article{arxiv.2210.09293,
title = {Learning Texture Transformer Network for Light Field Super-Resolution},
author = {Javeria Shabbir and M. Zeshan Alam and M. Umair Mukati},
journal= {arXiv preprint arXiv:2210.09293},
year = {2022}
}
Comments
European Conference on Visual Media Production (CVMP) 2022 short paper