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.

References in corpus (8)

• Noise2Noise: Learning Image Restoration without Clean Data
• Deep Image Prior
• Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
• Total Variation Denoising via the Moreau Envelope
• Quantum mechanics-based signal and image representation: application to denoising
• A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory
• Image Denoising Inspired by Quantum Many-Body physics
• Unsupervised Learning From Incomplete Measurements for Inverse Problems