Light Field Spatial Resolution Enhancement Framework

TL;DR

This paper introduces a modular framework for enhancing the spatial resolution of light field images, combining high-resolution image synthesis, texture transformation, and joint perspective refinement for superior results.

Contribution

It presents a novel, modular approach that improves light field resolution by integrating perspective-specific enhancement with global regularity constraints.

Findings

Outperforms existing light field resolution methods in quality metrics

Produces all-in-focus high-resolution images effectively

Enhances individual perspectives while maintaining light field consistency

Abstract

Light field (LF) imaging captures both angular and spatial light distributions, enabling advanced photographic techniques. However, micro-lens array (MLA)- based cameras face a spatial-angular resolution tradeoff due to a single shared sensor. We propose a novel light field framework for resolution enhancement, employing a modular approach. The first module generates a high-resolution, all-in-focus image. The second module, a texture transformer network, enhances the resolution of each light field perspective independently using the output of the first module as a reference image. The final module leverages light field regularity to jointly improve resolution across all LF image perspectives. Our approach demonstrates superior performance to existing methods in both qualitative and quantitative evaluations.