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Optics Letters

Optics Letters


  • Editor: Alan E. Willner
  • Vol. 34, Iss. 18 — Sep. 15, 2009
  • pp: 2835–2837

3D tensor factorization approach to single-frame model-free blind-image deconvolution

Ivica Kopriva  »View Author Affiliations

Optics Letters, Vol. 34, Issue 18, pp. 2835-2837 (2009)

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By applying a bank of 2D Gabor filters to a blurred image, single-frame blind-image deconvolution (SF BID) is formulated as a 3D tensor factorization (TF) problem, with the key contribution that neither origin nor size of the spatially invariant blurring kernel is required to be known or estimated. Mixing matrix, the original image, and its spatial derivatives are identified from the factors in the Tucker3 model of the multi channel version of the blurred image. Previous approaches to 2D Gabor-filter-bank-based SF BID relied on 2D representation of the multichannel version of the blurred image and matrix factorization methods such as nonnegative matrix factorization (NMF) and independent component analysis (ICA). Unlike matrix factorization-based methods 3D TF preserves local structure in the image. Moreover, 3D TF based on the PARAFAC model is unique up to permutation and scales under very mild conditions. To achieve this, NMF and ICA respectively require enforcement of sparseness and statistical independence constraints on the original image and its spatial derivatives. These constraints are generally not satisfied. The 3D TF-based SF BID method is demonstrated on an experimental defocused red–green–blue image.

© 2009 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(100.6640) Image processing : Superresolution
(100.6890) Image processing : Three-dimensional image processing

ToC Category:
Image Processing

Original Manuscript: March 31, 2009
Revised Manuscript: June 4, 2009
Manuscript Accepted: June 4, 2009
Published: September 14, 2009

Ivica Kopriva, "3D tensor factorization approach to single-frame model-free blind-image deconvolution," Opt. Lett. 34, 2835-2837 (2009)

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