Non-negative matrix factorization approach to blind image deconvolution

Authors:
Ivica Kopriva;Danielle Nuzillard
Affiliations:
Department of Electrical and Computer Engineering, The George Washington University, Washington DC;CReSTIC, Université de Reims – Chamapgne Ardenne (URCA), REIMS, France
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 3
Cited 0

Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
Iterative Identification and Restoration of Images (The International Series in Engineering and Computer Science)

Iterative Identification and Restoration of Images (The International Series in Engineering and Computer Science)
Blind deconvolution of images using optimal sparse representations

IEEE Transactions on Image Processing

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Abstract

A novel approach to single frame multichannel blind image deconvolution is formulated recently as non-negative matrix factorization (NMF) problem with sparseness constraint imposed on the unknown mixing vector. Unlike most of the blind image deconvolution algorithms, the NMF approach requires no a priori knowledge about the blurring kernel and original image. The experimental performance evaluation of the NMF algorithm is presented with the degraded image by the out-of-focus blur. The NMF algorithm is compared to the state-of-the-art single frame blind image deconvolution algorithm: blind Richardson-Lucy algorithm and single frame multichannel independent component analysis based algorithm. It has been demonstrated that NMF approach outperforms mentioned blind image deconvolution methods.