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.

翻译：我们提出回归基础（BTB）算法，一种用于噪声抑制的快速迭代方法。该算法计算高效，无需训练或真实数据，可适用于噪声水平未知的独立噪声及相关（相干）噪声场景。我们考察三个研究案例：加性高斯白噪声下的自然图像去噪、泊松分布图像去噪，以及光学相干断层扫描（OCT）中的散斑抑制。实验结果表明，该方法能在具有挑战性的噪声环境下有效提升图像质量。本文提供了收敛稳定性的理论保证。