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

Sayantan Dutta 1, 2 Adrian Basarab 2 Bertrand Georgeot 1, 3 Denis Kouamé 2
2 IRIT-MINDS - CoMputational imagINg anD viSion
IRIT - Institut de recherche en informatique de Toulouse
3 Information et Chaos Quantiques (LPT)
LPT - Laboratoire de Physique Théorique
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 solutions of quantum physics. The proposed algorithm referred to as QAB-PnP is well-adapted to Poisson noise, which is very common for imaging applications, such as, limited photon acquisition. In contrast to existing PnP approaches using a variance stabilizing transformation that is not invariant to deconvolution operation, the proposed method does not suffer from this theoretical problem. Moreover, 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.
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Preprints, Working Papers, ...
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Contributor : Bertrand Georgeot <>
Submitted on : Tuesday, October 20, 2020 - 10:44:34 AM
Last modification on : Monday, November 16, 2020 - 11:16:43 AM

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  • HAL Id : hal-02972150, version 1
  • ARXIV : 2010.09321


Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé. Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme. 2020. ⟨hal-02972150⟩



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