Light Field Super-Resolution: A Benchmark

Abstract

Lenslet-based light field imaging generally suffers from a fundamental trade-off between spatial and angular resolutions, which limits its promotion to practical applications. To this end, a substantial amount of efforts have been dedicated to light field super-resolution (SR) in recent years. Despite the demonstrated success, existing light field SR methods are often evaluated based on different degradation assumptions using different datasets, and even contradictory results are reported in literature. In this paper, we conduct the first systematic benchmark evaluation for representative light field SR methods on both synthetic and real-world datasets with various downsampling kernels and scaling factors. We then analyze and discuss the advantages and limitations of each kind of method from different perspectives. Especially, we find that CNN-based single image SR without using any angular information outperforms most light field SR methods even including learning-based ones. This benchmark evaluation, along with the comprehensive analysis and discussion, sheds light on the future researches in light field SR.