Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration

  • Hongwei Zheng
  • Olaf Hellwich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


The paper presents an unsupervised method for partially-blurred 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.


Point Spread Function Image Restoration Variable Exponent Blind Deconvolution Blur Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongwei Zheng
    • 1
  • Olaf Hellwich
    • 1
  1. 1.Computer Vision & Remote Sensing, Berlin University of TechnologyBerlin

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