Image Statistics and Local Spatial Conditions for Nonstationary Blurred Image Reconstruction

  • Hongwei Zheng
  • Olaf Hellwich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)


Deblurring is important in many visual systems. This paper presents a novel approach for nonstationary blurred image reconstruction with ringing reduction in a variational Bayesian learning and regularization framework. Our approach makes effective use of the image statistical prior and image local spatial conditions through the whole learning scheme. A nature image statistics based marginal prior distribution is used not only for blur kernel estimation but also for image reconstruction. For an ill-posed blur estimation problem, variational Bayesian ensemble learning can achieve a tractable posterior using an image statistic prior which is translation and scale-invariant. During the deblurring, nonstationary blurry images have stronger ringing effects. We thus propose an iterative reweighted regularization function based on the use of an image statistical prior and image local spatial conditions for perceptual image deblurring.


Natural Image Independent Component Analysis Image Restoration Kernel Estimation 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 2007

Authors and Affiliations

  • Hongwei Zheng
    • 1
  • Olaf Hellwich
    • 1
  1. 1.Computer Vision & Remote Sensing, Berlin University of Technology, Franklinstrasse 28/29, Office FR 3-1, D-10587 Berlin 

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