Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration

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Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration
Abstract. The paper presents an unsupervised method for partiallyblurred image restoration without influencing unblurred regions or objects. Maximum a posteriori estimation of parameters in Bayesian regularization is equal to minimizing energy of a dataset for a given number of classes. To estimate the point spread function (PSF), a parametric model space is introduced to reduce the searching uncertainty for PSF model selection. Simultaneously, PSF self-initializing does not rely on supervision or thresholds. In the image domain, a gradient map as a priori knowledge is derived not only for dynamically choosing nonlinear diffusion operators but also for segregating blurred and unblurred regions via an extended graph-theoretic method. The cost functions with respect to the image and the PSF are alternately minimized in a convex manner. The algorithm is robust in that it can handle images that are formed in variational environments with different blur and stronger noise.
Hongwei Zheng, Olaf Hellwich
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where DAGM
Authors Hongwei Zheng, Olaf Hellwich
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