Joint image denoising using light-field data

In this paper we introduce a new framework for exploiting machine learning principles in the processing of light-field imagery, bypassing the explicit recovery of scene depth. As an application here, we jointly denoise all images within a light-field collection by taking into consideration the implications of scene structure on the raw image information. Our experimental results demonstrate significant performance improvement over the state-of-art single image denoising algorithms.