A NOVEL UNSUPERVISED DECONVOLUTION NETWORK : EFFICIENT FOR A SPARSE SOURCE
Seungjin Choi · 한국정보과학회 학술발표논문집 · 1998
This paper presents a novel neural network structure to the blind deconvolution task where the input (source) to a system is not available and the source has any type of distribution including sparse distribution. We employ multiple sensors so that spatial information plays a important role. The resulting learning algorithm is linear so that it works for both sub- and super-Gaussian source. Moreover, we can successfully deconvolve the mixture of a sparse source, while most existing algorithms [5] have difficulties in this task. Computer simulations confirm the validity and high performance of the proposed algorithm.