Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images

TL;DR

This paper introduces a novel plug-and-play quantum denoiser integrated into an ADMM framework for effective Poisson image deconvolution, demonstrating superior adaptability and convergence without prior noise statistics.

Contribution

The work presents the first integration of a quantum physics-based adaptive denoiser into a PnP-ADMM scheme for Poisson noise removal, with proven convergence and improved performance.

Findings

Outperforms state-of-the-art techniques in various noise scenarios

Demonstrates robustness without requiring prior noise statistics

Proves convergence of the quantum denoising integrated algorithm

Abstract

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm.