Single-frame multichannel blind deconvolution by nonnegative matrix factorization with sparseness constraints

Abstract

Single-frame multichannel blind deconvolution is formulated by applying a bank of Gabor filters to a blurred image. The key observation is that spatially oriented Gabor filters produce sparse images and that a multichannel version of the observed image can be represented as a product of an unknown nonnegative sparse mixing vector and an unknown nonnegative source image. Therefore a blind-deconvolution problem is formulated as a nonnegative matrix factorization problem with a sparseness constraint. No a priori knowledge about the blurring kernel or the original image is required. The good experimental results demonstrate the viability of the proposed concept.