2019/02/25 by Antoine Paris, Laurent Jacques, Paris, Antoine +1
Computer Science · Engineering · #Blind Source Separation Techniques #FOS: Electrical engineering #Image and Signal Denoising Methods #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1902.09151
10 pages, 4 figures, accepted at EUROCON 2019 as part of the IEEE Region 8 Student Paper Contest
openalex publication_date 2019/02/25 · arxiv created 2019/02/26 · arxiv updated 2019/02/27 · openalex created_date 2019/03/02 · openalex updated_date 2026/07/28
This work considers the multi-channel blind deconvolution problem under the assumption that the channels are short. First, we investigate the ill-posedness issues inherent to blind deconvolution problems and sufficient and necessary conditions on the channels that guarantee well-posedness are derived. Following previous work on blind deconvolution, the problem is then reformulated as a low-rank matrix recovery problem and solved by nuclear norm minimization. Numerical experiments show the effectiveness of this algorithm under a certain generative model for the input signal and the channels, both in the noiseless and in the noisy case.