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Deblurring-by-Denoising using Spatially Adaptive Gaussian Scale Mixtures in Overcomplete Pyramids

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2 Author(s)
Guerrero-Colon, J.A. ; Visual Inf. Process. Group, Granada Univ., Spain ; Portilla, J.

In a previous work, we presented an extension of the original Bayes least squares-Gaussian scale mixtures (BLS-GSM) denoising algorithm that also compensated the blur. However, that method had some problems: a) it could not compensate for some blurring kernels; b) its performance depended critically on having an accurate estimation of the original power spectral density (PSD); and c) it could not be easily adapted to a spatially variant description of the image statistics. In this work we propose a two-step restoration method that overcomes these problems by first performing a global blur image compensation, and then applying a spatially adaptive local denoising, in an overcomplete pyramid. Our method is efficient, robust and non-iterative. We demonstrate through simulations that it provides state-of-the-art performance.

Published in:

Image Processing, 2006 IEEE International Conference on

Date of Conference:

8-11 Oct. 2006

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