Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme

TL;DR

This paper presents a novel Plug-and-Play ADMM algorithm utilizing a quantum physics-based denoiser for Poisson image deconvolution, demonstrating superior performance in various noise conditions.

Contribution

It introduces a quantum physics-inspired denoiser within a PnP ADMM framework for Poisson deconvolution, enhancing flexibility and effectiveness over existing methods.

Findings

Outperforms recent state-of-the-art techniques in deconvolution tasks.

Effective in both low and high SNR scenarios.

Quantum denoiser provides adaptable noise handling.

Abstract

This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics. The efficiency of the proposed algorithm is evaluated for Poisson image deconvolution, which is very common for imaging applications, such as, for example, limited photon acquisition. Numerical results show the superiority of the proposed scheme compared to recent state-of-the-art techniques, for both low and high signal-to-noise-ratio scenarios. This performance gain is mostly explained by the flexibility of the embedded quantum denoiser for different types of noise affecting the observations.