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Variational blind deconvolution of multi‐channel images
Author(s) -
Kaftory Ran,
Sochen Nir,
Zeevi Yehushua Y.
Publication year - 2005
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20038
Subject(s) - deblurring , deconvolution , blind deconvolution , smoothing , computer science , regularization (linguistics) , robustness (evolution) , algorithm , kernel (algebra) , channel (broadcasting) , artificial intelligence , mathematical optimization , mathematics , image restoration , computer vision , image (mathematics) , image processing , telecommunications , biochemistry , chemistry , combinatorics , gene
The fundamental problem of denoising and deblurring images is addressed in this study. The great difficulty in this task is due to the ill‐posedness of the problem. We analyze multi‐channel images to gain robustness and regularize the process by the Polyakov action, which provides an anisotropic smoothing term that uses inter‐channel information. Blind deconvolution is then solved by an additional anisotropic regularization term of the same type for the kernel. It is shown that the Beltrami regularizer leads to better results than the total variation (TV) regularizer. An analytic comparison to the TV method is carried out and results on synthetic and real data are demonstrated. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 56–63, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20038