Techniques for fast, 3D, quantitative microscopy are of great interest in many fields. In this context, in-line digital holography has significant potential due to its relatively simple setup (lensless imaging), its three-dimensional character and its temporal resolution. The goal of this thesis is to improve existing hologram reconstruction techniques by employing an "inverse problems" approach. For applications of objects with parametric shapes, a greedy algorithm has been previously proposed which solves the (inherently ill-posed) inversion problem of reconstruction by maximizing the likelihood between a model of holographic patterns and the measured data. The first contribution of this thesis is to reduce the computational costs of this algorithm using a multi-resolution approach (FAST algorithm). For the second contribution, a "matching pursuit" type of pattern recognition approach is proposed for hologram reconstruction of volumes containing parametric objects, or non-parametric objects of a few shape classes. This method finds the closest set of diffraction patterns to the measured data using a diffraction pattern dictionary. The size of the dictionary is reduced by employing a truncated singular value decomposition to obtain a low cost algorithm. The third contribution of this thesis was carried out in collaboration with the laboratory of fluid mechanics and acoustics of Lyon (LMFA). The greedy algorithm is used in a real application: the reconstruction and tracking of free-falling, evaporating, ether droplets. In all the proposed methods, special attention has been paid to improvement of the accuracy of reconstruction as well as to reducing the computational costs and the number of parameters to be tuned by the user (so that the proposed algorithms are used with little or no supervision). A Matlab® toolbox (accessible on-line) has been developed as part of this thesis
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-01004605 |
| Date | 03 October 2013 |
| Creators | Seifi, Mozhdeh |
| Publisher | Université Jean Monnet - Saint-Etienne |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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