Computer Vision – ECCV 2014

Heavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem. Existing synthetic aperture imaging methods, however, emulate focusing at a specific depth layer but is incapable of producing an all-in-focus see-through image. Alternative in-painting algorithms can generate visually plausible results but can not guarantee the correctness of the result. In this paper, we present a novel depth free all-in-focus SAI technique based on lightfield visibility analysis. Specifically, we partition the scene into multiple visibility layers to directly deal with layer-wise occlusion and apply an optimization framework to propagate the visibility information between multiple layers. On each layer, visibility and optimal focus depth estimation is formulated as a multiple label energy minimization problem. The energy integrates the visibility mask from previous layers, multi-view intensity consistency, and depth smoothness constraint. We compare our method with the state-of-the-art solutions. Extensive experimental results with qualitative and quantitative analysis demonstrate the effectiveness and superiority of our approach.