William is a PhD student in the Bioimage Analysis Unit under the joint supervision of Elsa Angelini (LTCI, Telecom ParisTech, Paris) and Jean-Christophe Olivo-Marin (Bioimage Analysis). His work consists in investigating on application of the Compressed Sensing (CS) theory in the field of biological microscopy. This mathematical framework states that it is possible, under certain constraints, to reconstruct a high-quality image from very fewer samples than the full acquisition. His first results have consisted in the development of a CS-based denoising algorithm, which led to segmentation results in the context of SD-OCT imaging. His current work is more theory-based, as he is seeking for fundamental results to apply CS in new microscopy experiments.
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Compressive Sensing in biological imaging
Jean-Christophe Olivo-Marin (PI)
We are investigating a new global framework for smart acquisition methods in biological imaging whereby it is possible with mathematical tools to recover a high quality image from very fewer samples than the full […]
2018Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method, IEEE Transactions on Image Processing, 2018, vol. 27, no 8, p. 3842-3856.
2017A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images, Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039406 (24 August 2017); doi: 10.1117/12.2274126;.
2017Reducing data acquisition for fast structured illumination microscopy using compressed sensing, 10.1109/ISBI.2017.7950461.
2016Sparsity-based simplification of spectral-domain optical coherence tomography images of cardiac samples, 10.1109/ISBI.2016.7493286.
2015Image denoising by adaptive Compressed Sensing reconstructions and fusions, Proc. SPIE 9597, Wavelets and Sparsity XVI, 95970X (September 11, 2015); doi:10.1117/12.2188648.
2015Image denoising by multiple Compressed Sensing reconstructions, 10.1109/ISBI.2015.7164096.
2014Adaptive confidence bands in the nonparametric fixed design regression model, Journal of Nonparametric Statistics, 26(3), 451-469. ISO 690.