Back to Basics: Fast Denoising Iterative Algorithm
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Abstract (arXiv)
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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References · 4
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2023Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restorationcited by 165
- 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
- 2022Less is More: Rethinking Few-Shot Learning and Recurrent Neural Netscited by 3
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