Zobrazeno 1 - 10
of 172
pro vyhledávání: '"Kouamé, Denis"'
Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequenc
Externí odkaz:
http://arxiv.org/abs/2409.13634
Background noise in many fields such as medical imaging poses significant challenges for accurate diagnosis, prompting the development of denoising algorithms. Traditional methodologies, however, often struggle to address the complexities of noisy en
Externí odkaz:
http://arxiv.org/abs/2405.12226
Publikováno v:
IEEE Signal Processing Letters, 2023
This letter presents a novel \textit{quantum algorithm} for signal denoising, which performs a thresholding in the frequency domain through amplitude amplification and using an adaptive threshold determined by local mean values. The proposed algorith
Externí odkaz:
http://arxiv.org/abs/2312.15411
Autor:
Sanchez, Karen, Hinojosa, Carlos, Arias, Kevin, Arguello, Henry, Kouame, Denis, Meyrignac, Olivier, Basarab, Adrian
Data augmentation is classically used to improve the overall performance of deep learning models. It is, however, challenging in the case of medical applications, and in particular for multiparametric datasets. For example, traditional geometric tran
Externí odkaz:
http://arxiv.org/abs/2307.16314
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. Furthermore, it is shown that with very slight modifications, this network can be e
Externí odkaz:
http://arxiv.org/abs/2301.00247
Publikováno v:
Signal Processing, Volume 201, 2022, 108690
Sparse representation of real-life images is a very effective approach in imaging applications, such as denoising. In recent years, with the growth of computing power, data-driven strategies exploiting the redundancy within patches extracted from one
Externí odkaz:
http://arxiv.org/abs/2112.09254
Publikováno v:
In Pattern Recognition November 2024 155
Autor:
Mouret, Florian, Albughdadi, Mohanad, Duthoit, Sylvie, Kouamé, Denis, Rieu, Guillaume, Tourneret, Jean-Yves
Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning technique
Externí odkaz:
http://arxiv.org/abs/2110.11780
Publikováno v:
IEEE International Conference on Image Processing (ICIP 2021)
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
Externí odkaz:
http://arxiv.org/abs/2108.13778
Publikováno v:
IEEE Access, vol. 9, pp. 139771-139791, 2021
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
Externí odkaz:
http://arxiv.org/abs/2107.00407