Deblending VIA Regularization by Denoising
Breno Bahia, Mauricio D. Sacchi · 2021
Summary Simultaneous source separation, or deblending, techniques can be classified in inversion or denoising-based methods. This paper exploits the advances in both of these classes and proposes to bring them together to formulate a powerful deblending approach based on regularization by denoising. Such an approach poses the inverse problem through a clear and comprehensible cost function where a regularization term employs denoising techniques in its definition. Through numerically blended real data examples, this paper shows that RED can achieve good deblending results.