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Proceedings Paper

Image denoising by adaptive Compressed Sensing reconstructions and fusions
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Paper Abstract

In this work, Compressed Sensing (CS) is investigated as a denoising tool in bioimaging. The denoising algorithm exploits multiple CS reconstructions, taking advantage of the robustness of CS in the presence of noise via regularized reconstructions and the properties of the Fourier transform of bioimages. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using several sparsity constraints. We present different combination methods for the CS reconstructions and quantitatively compare the performance of our denoising methods to state-of-the-art ones.

Paper Details

Date Published: 11 September 2015
PDF: 13 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 95970X (11 September 2015); doi: 10.1117/12.2188648
Show Author Affiliations
William Meiniel, Institut Pasteur, CNRS (France)
Institut Telecom, Telecom ParisTech, CNRS (France)
Elsa Angelini, Institut Telecom, Telecom ParisTech, CNRS (France)
Jean-Christophe Olivo-Marin, Institut Pasteur, CNRS (France)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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