  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.
