Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth
Abstract
Denoising is omnipresent in image processing. It is usually addressed with algorithms relying on a set of hyperparameters that control the quality of the recovered image. Manual tuning of those parameters can be a daunting task, which calls for the development of automatic tuning methods. Given a denoising algorithm, the best set of parameters is the one that minimizes the error between denoised and ground-truth images. Clearly, this ideal approach is unrealistic, as the ground-truth images are unknown in practice. In this work, we propose unsupervised cost functions -- i.e., that only require the noisy image -- that allow us to reach this ideal gold standard performance. Specifically, the proposed approach makes it possible to obtain an average PSNR output within less than 1% of the best achievable PSNR.
Cite
@article{arxiv.2401.09817,
title = {Automatic Tuning of Denoising Algorithms Parameters Without Ground Truth},
author = {Arthur Floquet and Sayantan Dutta and Emmanuel Soubies and Duong Hung Pham and Denis Kouame},
journal= {arXiv preprint arXiv:2401.09817},
year = {2024}
}