Image denoising by adaptive Compressed Sensing reconstructions and fusions

William Meiniel; Elsa Angelini; Jean-Christophe Olivo-Marin

doi:10.1117/12.2188648

11 September 2015 Image denoising by adaptive Compressed Sensing reconstructions and fusions

William Meiniel, Elsa Angelini, Jean-Christophe Olivo-Marin

Proceedings Volume 9597, Wavelets and Sparsity XVI; 95970X (2015) https://doi.org/10.1117/12.2188648
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

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.

Citation Download Citation

William Meiniel, Elsa Angelini, and Jean-Christophe Olivo-Marin "Image denoising by adaptive Compressed Sensing reconstructions and fusions", Proc. SPIE 9597, Wavelets and Sparsity XVI, 95970X (11 September 2015); https://doi.org/10.1117/12.2188648

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