2021
DOI: 10.1190/geo2021-0068.1
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Regularization by denoising for simultaneous source separation

Abstract: Denoisers can help solve inverse problems via a recently proposed framework known as regularization by denoising (RED). The RED approach defines the regularization term of the inverse problem via explicit denoising engines. Simultaneous source separation techniques, being themselves a combination of inversion and denoising methods, provide a formidable field to explore RED. We investigate the applicability of RED to simultaneous-source data processing and introduce a deblending algorithm named REDeblending (RD… Show more

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Cited by 7 publications
(2 citation statements)
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“…All the deep learning approaches to date use CNNs and require pre-training. [4] uses the RED framework introduced in [37], which is similar to the PnP framework. The difference is that RED explicitly incorporates the denoiser into the objective function.…”
Section: Related Workmentioning
confidence: 99%
“…All the deep learning approaches to date use CNNs and require pre-training. [4] uses the RED framework introduced in [37], which is similar to the PnP framework. The difference is that RED explicitly incorporates the denoiser into the objective function.…”
Section: Related Workmentioning
confidence: 99%
“…The filtering‐based method is based on the assumption that the source with random excitation times is random noise after transforming the data into frequency–wavenumber (FK) domain or rearranging the data into common offset gathers. Median filtering (Chen et al., 2020b), radon transform (Lin & Sacchi, 2020) and FK filtering (Bahia et al., 2021) are common filtering‐based tools for deblending. The inversion‐based deblending method adopts an iterative way to attenuate the blending noise gradually (Ibrahim & Trad, 2020).…”
Section: Introductionmentioning
confidence: 99%