Light Field Spatial Resolution Enhancement Framework
This addresses resolution limitations in light field imaging for applications like photography and computational imaging, but it is incremental as it builds on existing enhancement methods.
The paper tackles the spatial-angular resolution tradeoff in micro-lens array-based light field cameras by proposing a modular framework that generates a high-resolution all-in-focus image and uses a texture transformer network to enhance each perspective, achieving superior performance in qualitative and quantitative evaluations.
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.