Learning Texture Transformer Network for Light Field Super-Resolution
This addresses the spatio-angular tradeoff problem for light field imaging, but appears incremental as it builds on transformer networks for a specific domain.
The paper tackled the low spatial resolution of hand-held light field cameras by proposing a Texture Transformer Network (TTSR) method, achieving a 4 dB to 6 dB PSNR gain over bicubically resized images.
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