Back to Basics: Fast Denoising Iterative Algorithm
This provides a fast, training-free denoising method for image processing applications, but it appears incremental as it builds on iterative algorithms without a major paradigm shift.
The paper tackles the problem of image denoising under various noise types, including additive white Gaussian, Poisson, and speckle noise, without requiring training data, and demonstrates effective quality improvement in challenging settings with theoretical convergence guarantees.
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.