Extracting Depth and Radiance From a Defocused Video Pair

摘要

We present a novel iterative feedback approach for the simultaneous estimation of depth and all-in-focus (AIF) videos from a defocused video pair by joint spatiotemporal optimization. Depth and AIF videos benefit each other in the iterative optimization. First, for the recovery of AIF video, the sparse prior of natural video is incorporated to ensure a high-quality defocus blur removal even under inaccurate depth estimation. Second, in depth estimation step, we feed back the spatial and temporal constraints from the high-quality AIF video and adopt a numerical solution, which is robust to the inaccuracy of AIF recovery to further boost the performance of depth from the defocus algorithm. Benefitting from the incorporation of AIF video priors and the temporal consistency constraint, the proposed framework can effectively reconstruct the depth of the textureless region and is insensitive to camera parameter changes. Our approach provides better temporal consistency and higher depth accuracy than the conventional method that applies postsmoothing to the sequential frame estimation. We not only demonstrate the feasibility of our approach via real experimentation but also provide visual and quantitative evaluation on synthetic data.