A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory

TL;DR

This paper introduces a novel image denoising algorithm inspired by quantum many-body theory, leveraging an adaptive, image-dependent basis to effectively preserve structures across various noise scenarios and image types.

Contribution

The proposed method uniquely applies quantum mechanics concepts to image denoising, creating an adaptive basis that handles diverse noise conditions without parameter adjustments.

Findings

Demonstrates superior denoising performance across different images and noise types.

Effectively preserves local image structures during denoising.

Successfully applied to medical ultrasound image despeckling.

Abstract

Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one or several images to increase sparsity have become more prominent. This paper presents a novel image denoising algorithm exploiting such an image-dependent basis inspired by the quantum many-body theory. Based on patch analysis, the similarity measures in a local image neighborhood are formalized through a term akin to interaction in quantum mechanics that can efficiently preserve the local structures of real images. The versatile nature of this adaptive basis extends the scope of its application to image-independent or image-dependent noise scenarios without any adjustment. We carry out a rigorous comparison with contemporary methods to demonstrate the denoising capability of the proposed algorithm regardless of the image characteristics, noise statistics and intensity. We illustrate the properties of the hyperparameters and their respective effects on the denoising performance, together with automated rules of selecting their values close to the optimal one in experimental setups with ground truth not available. Finally, we show the ability of our approach to deal with practical images denoising problems such as medical ultrasound image despeckling applications.