Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Idier, Jérome"'
Ultrasound imaging, despite its widespread use in medicine, often suffers from various sources of noise and artifacts that impact the signal-to-noise ratio and overall image quality. Enhancing ultrasound images requires a delicate balance between con
Externí odkaz:
http://arxiv.org/abs/2409.11380
Despite today's prevalence of ultrasound imaging in medicine, ultrasound signal-to-noise ratio is still affected by several sources of noise and artefacts. Moreover, enhancing ultrasound image quality involves balancing concurrent factors like contra
Externí odkaz:
http://arxiv.org/abs/2403.15316
Autor:
Labouesse, Simon, Idier, Jérôme, Allain, Marc, Giroussens, Guillaume, Mangeat, Thomas, Sentenac, Anne
Improving the resolution of fluorescence microscopy beyond the diffraction limit can be achievedby acquiring and processing multiple images of the sample under different illumination conditions.One of the simplest techniques, Random Illumination Micr
Externí odkaz:
http://arxiv.org/abs/2311.13897
Despite its wide use in medicine, ultrasound imaging faces several challenges related to its poor signal-to-noise ratio and several sources of noise and artefacts. Enhancing ultrasound image quality involves balancing concurrent factors like contrast
Externí odkaz:
http://arxiv.org/abs/2310.20618
This contribution addresses the problem of image reconstruction of radioactivity distribution for which the available information arises from several classes of data, each associated with a specific combination of detections. We introduce a theoretic
Externí odkaz:
http://arxiv.org/abs/2309.05324
Ultrasound image reconstruction can be approximately cast as a linear inverse problem that has traditionally been solved with penalized optimization using the $l_1$ or $l_2$ norm, or wavelet-based terms. However, such regularization functions often s
Externí odkaz:
http://arxiv.org/abs/2307.15990
Recently, it has been shown theoretically that fluorescence microscopy using random illuminations (RIM) yields a doubled lateral resolution and an improved optical sectioning. Moreover, an algorithm called algoRIM, based on variance matching, has bee
Externí odkaz:
http://arxiv.org/abs/2103.00493
In this paper the problem of restoration of non-negative sparse signals is addressed in the Bayesian framework. We introduce a new probabilistic hierarchical prior, based on the Generalized Hyperbolic (GH) distribution, which explicitly accounts for
Externí odkaz:
http://arxiv.org/abs/2102.06081
In this paper, we propose a new greedy algorithm for sparse approximation, called SLS for Single L_1 Selection. SLS essentially consists of a greedy forward strategy, where the selection rule of a new component at each iteration is based on solving a
Externí odkaz:
http://arxiv.org/abs/2102.06058
In a low-statistics PET imaging context, the positive bias in regions of low activity is a burning issue. To overcome this problem, algorithms without the built-in non-negativity constraint may be used. They allow negative voxels in the image to redu
Externí odkaz:
http://arxiv.org/abs/2012.06432