# Back to Basics: Fast Denoising Iterative Algorithm

arXiv: https://arxiv.org/abs/2311.06634 · HTML: https://mlsanity.com/paper/2311.06634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration](https://mlsanity.com/paper/2303.11435/) (2023)
- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets](https://mlsanity.com/paper/2209.14267/) (2022)

## 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.