Development and implementation of compressed sensing-based denoising and acquisition strategies for fluorescence microscopy and optical coherence tomography

Overview & Synopsis

The mathematical theory of Compressed Sensing (CS) is a recently developed framework that enables the reconstruction of a signal or an image from very few measurements. In this thesis, we investigate how this theory can be implemented in the context of two optical microscopy techniques : fluorescence microscopy, and optical coherence tomography. Both technologies present different limitations which we prove can be tackled by the embedding of CS driven strategies. The latter can be divided into two categories : image processing algorithmic solutions, and optical acquisition techniques.