2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-1.2
Paper Title Image Denoising Inspired by Quantum Many-Body physics
Authors Sayantan Dutta, Adrian Basarab, Institut de Recherche en Informatique de Toulouse, UMR CNRS 5505, Université de Toulouse, France; Bertrand Georgeot, Laboratoire de Physique Théorique, Université de Toulouse, CNRS, UPS, France; Denis Kouamé, Institut de Recherche en Informatique de Toulouse, UMR CNRS 5505, Université de Toulouse, France
SessionTEC-1: Restoration and Enhancement 1
LocationArea G
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches.