Progressive MAP-based Deconvolution with Pixel-Dependent Gaussian Prior

A deconvolution is a fundamental technique and used in various vision applications. A maximum a posteriori estimation is known as a powerful tool. In this paper, we propose a progressive MAP-based deconvolution algorithm with a pixel dependent Gaussian image prior. In the proposed algorithm, a mean and a variance for each pixel are adaptively estimated. Then, the mean and the variance are progressively updated. We experimentally show that the proposed algorithm is comparable to the state-of-the-art algorithms in the case that the true point spread function (PSF) is used for the deconvolution, and that the proposed algorithm outperforms in the non-true PSF case.