A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

We present a sparse reconstruction light field (SRLF) framework using compressed sensing theory. Light field signals have sparsity in some specific phenomena, such as nonocclusions, smooth surfaces and texture uniformity. We study the light field sparsity to analyze the sparse basis and measurement matrix of the light field in a complicated scene. This extends previous work on light field sampling that considered either spatial or angular dimensions, which can be used to control the sampling rate of the light field. Furthermore, the sparse Bayes learning (SBL) algorithm is applied to the reconstruction of sparsely sampled light fields. We derive a learning machine for the light field SBL algorithm, which can improve the rendering quality based on a given set of captured multiview images. The proposed SRLF compares favorably with state-of-the-art light field sampling and reconstruction techniques. The innovation of the SRLF is to use compressed sensing theory to further reduce the light field sampling rate. We conduct a detailed derivation of the mathematical theory of light field sparseness.