no code implementations • 31 Dec 2022 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics.
no code implementations • 16 Dec 2021 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising.
no code implementations • 31 Aug 2021 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
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.
1 code implementation • 1 Jul 2021 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
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.
no code implementations • 19 Oct 2020 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
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.
no code implementations • 2 Apr 2020 • Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé
Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis.