Image Denoising Inspired by Quantum Many-Body physics

TL;DR

This paper introduces a novel image denoising method inspired by quantum many-body physics, utilizing an adaptive basis constructed through quantum interaction principles, achieving competitive results in noise reduction.

Contribution

It presents an original approach to image denoising by incorporating quantum physics concepts into the basis construction process, a novel idea in image processing.

Findings

Achieves comparable or slightly better denoising results than existing methods.

Demonstrates effectiveness on images corrupted with additive white Gaussian noise.

Potential applicability to other noise types beyond Gaussian.

Abstract

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches.