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Image denoising by multiple Compressed Sensing reconstructions

William Meiniel 1, 2 Yoann Le Montagner 1, 2 Elsa D. Angelini 3 J.-C. Olivo-Marin
1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse
LTCI - Laboratoire Traitement et Communication de l'Information
2 TSI - Département Traitement du Signal et des Images
3 LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : In this paper, compressed sensing (CS) is investigated as a denoising tool in bioimaging. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using total-variation sparsity constraints. The validity of the proposed method is first assessed on a synthetic image with a known ground truth and then applied to real biological images.
Keywords : Imagerie biologique Compressed Sensing Débruitage
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Conference papers
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https://hal.telecom-paris.fr/hal-02288452
Contributor : Telecomparis Hal <>
Submitted on : Saturday, September 14, 2019 - 6:50:05 PM
Last modification on : Wednesday, September 30, 2020 - 8:54:14 AM

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LTCI | CNRS | PARISTECH | INSTITUT-TELECOM | IDS | IMAGES | UNIV-PARIS-SACLAY

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William Meiniel, Yoann Le Montagner, Elsa D. Angelini, J.-C. Olivo-Marin. Image denoising by multiple Compressed Sensing reconstructions. IEEE International Symposium on Biomedial Imaging, Apr 2015, New York, United States. pp.1232-1235, ⟨10.1109/ISBI.2015.7164096⟩. ⟨hal-02288452⟩

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