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Blind Blur Estimation Using Low Rank Approximation of Cepstrum

  • Adeel A. Bhutta
  • Hassan Foroosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

The quality of image restoration from degraded images is highly dependent upon a reliable estimate of blur. This paper proposes a blind blur estimation technique based on the low rank approximation of cepstrum. The key idea that this paper presents is that the blur functions usually have low ranks when compared with ranks of real images and can be estimated from cepstrum of degraded images. We extend this idea and propose a general framework for estimation of any type of blur. We show that the proposed technique can correctly estimate commonly used blur types both in noiseless and noisy cases. Experimental results for a wide variety of conditions i.e., when images have low resolution, large blur support, and low signal-to-noise ratio, have been presented to validate our proposed method.

Keywords

Singular Value Decomposition Gaussian Mixture Model Singular Vector Motion Blur Degraded Image 
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

  • Adeel A. Bhutta
    • 1
  • Hassan Foroosh
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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