This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (DeQuIP) [see the DeQuIP_flow.pdf for a basic working principle], relying on the theory of quantum many-body physics.
Sample code of the papers:
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "DIVA: Deep Unfolded Network from Quantum Interactive Patches for Image Restoration," Submitted, 2022.
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "Deep Unfolding of Image Denoising by Quantum Interactive Patches," 2022 IEEE International Conference on Image Processing (ICIP), 2022.
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "A Novel Image Denoising Algorithm Using Concepts of Quantum Many-Body Theory," Signal Processing, vol. 201, pp. 108690, 2022, doi: 10.1016/j.sigpro.2022.108690
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "Image Denoising Inspired by Quantum Many-Body physics," 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1619-1623, doi: 10.1109/ICIP42928.2021.9506794.
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "Quantum mechanics-based signal and image representation: Application to denoising," IEEE Open Journal of Signal Processing, vol. 2, pp. 190–206, 2021.
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Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, and Denis Kouamé, "Despeckling Ultrasound Images Using Quantum Many-Body Physics," 2021 IEEE International Ultrasonics Symposium (IUS), 2021, pp. 1-4, doi: 10.1109/IUS52206.2021.9593778.
Python code prepard by Sayantan Dutta E-mail: sdu4004@med.cornell.edu and sayantan.dutta110@gmail.com
The following script shows an example of our image restoration algorithm Deep denoising by quantum InteractiVe pAtches (DIVA)