Title :
Image denoising by multiple compressed sensing reconstructions
Author :
Meiniel, William ; Le Montagner, Yoann ; Angelini, Elsa ; Olivo-Marin, Jean-Christophe
Author_Institution :
Unite d´Anal. d´Images Biol., Inst. Pasteur, France
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 spar-sity 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 :
compressed sensing; image denoising; image reconstruction; medical image processing; bioimaging; high quality denoised images; image denoising; low sampling rate; multiple compressed sensing reconstructions; synthetic image; total variation sparsity constraints; Biology; Compressed sensing; Image reconstruction; Noise; Noise reduction; Signal processing algorithms; TV; Bioimaging; Fourier transform; compressed sensing; denoising; total-variation;
Conference_Titel :
Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
Conference_Location :
New York, NY
DOI :
10.1109/ISBI.2015.7164096
