Non-negative Matrix Factorization Approach to Blind Image Deconvolution

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.