This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-in-Focus (AIF) image, which benefits each other in each stage to obtain final convergence: For the recovery of AIF image, sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation. In depth estimation step, we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm. Compared with traditional DFD methods, another advantage offered by this iterative framework is that by introducing AIF, which follows the prior knowledge of natural images to regularize the depth map estimation, DFD is much more robust to camera parameter changes. In addition, the proposed approach is a general framework that can incorporate depth estimation and AIF image recovery algorithms. The experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method, especially on the challenging data sets containing large textureless regions and within a large range of camera parameters.
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