Back to Basics: Fast Denoising Iterative Algorithm

Deborah Pereg

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arXiv · 2311.06634

Back to Basics: Fast Denoising Iterative Algorithm

Deborah Pereg

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Abstract

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.

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Deborah Pereg. 2024-04-18. Back to Basics: Fast Denoising Iterative Algorithm. https://arxiv.org/abs/2311.06634

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