Dynamic light field reconstruction via densely connected deep equilibrium model

Abstract

High-resolution consumer plenoptic cameras usually feature low frame rates, making them not well-suited for capturing high-speed motion scenes. To compensate for this limitation, we extend the original snapshot compressive imaging system to plenoptic cameras and propose a densely connected deep equilibrium (DEQ) model for high-quality dynamic light field (LF) reconstruction, abbreviated as DLFDEQ. Specifically, we perform temporal compression encoding on a dynamic LF and model the reconstruction process as an inverse problem with an implicit regularization term. To solve this inverse problem, we present a densely connected DEQ model based on gradient descent. Our approach demonstrates stronger robustness and better detail retention than existing methods. We can practically quadruple the original camera’s frame rate by continually capturing and retrieving these measurement frames with high reconstruction accuracy.