Back to basics: Fast denoising iterative algorithm

We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknow...

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Bibliographic Details
Published in:Signal processing Vol. 221; p. 109482
Main Author: Pereg, Deborah
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2024
Subjects:
Fixed-point
Image denoising
Inverse problems
Speckle suppression
Speckle suppression
Inverse problems
Fixed-point
Image denoising
ISSN:0165-1684, 1872-7557
Online Access:Get full text
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Summary:We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction. Our method is computationally efficient, does not require training or ground truth data, and can be applied in the presence of independent noise, as well as correlated (coherent) noise, where the noise level is unknown. We examine three study cases: natural image denoising in the presence of additive white Gaussian noise, Poisson-distributed image denoising, and speckle suppression in optical coherence tomography (OCT). Experimental results demonstrate that the proposed approach can effectively improve image quality, in challenging noise settings. Theoretical guarantees are provided for convergence stability. •We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction.•Our method is computationally efficient and does not require ground truth data.•BTB can mitigate independent or correlated noise with unknown noise level.•Experimental results demonstrate improved image quality, in challenging noise settings.•Theoretical guarantees are provided for convergence stability.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2024.109482