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Abstract
Imaging polarimetry has shown its advantage over other imaging techniques on various sensing tasks such as astronomical sensing, atmospheric sensing, target detection and biomedical diagnostics because it provides information of the imaged scene that are not obtainable to other imaging modalities such as shading and surface roughness of objects. Polarimeters are the devices designed to measure the polarization of light. In general imaging polarimeters can be divided into two broad classes, the wavefront division polarimeters and the modulated polarimeters. This thesis aims to contribute to the development of modulated polarimeters.
The essential weakness of the modulated polarimeter is the resolution loss caused by the bandwidth reduction since this class of polarimeters divide the native bandwidth of the underlying detector to measure multiple pieces of polarization information “simultaneously”. In previous studies, researchers attempted to mitigate this resolution loss by designing more advanced modulation schemes that divide the native bandwidth in more economical fashions or combining multiple domains in modulation to expand the bandwidths of the polarimeters by trading off bandwidth of different
domains. In recent years, our group and others have started looking at the latter approach, but it is still not particularly popular in the wider community. Many advanced multi-domain modulated polarimeter designs have been proposed but have not been verified with experiments. Therefore, the first study presented in this thesis demonstrates an experimental work where a previously proposed spatio-temporally modulated polarimeter is constructed and its advantages over single domain modulated polarimeters are verified.
Secondly, this thesis examines the data reconstruction procedure of modulated polarimeters and proposes a novel machine learning based adaptive data reconstruction framework. This adaptive framework is demonstrated with a simulated classic spatially modulated polarimeter and compared with the conventional data reconstruction methods. The adaptive framework shows a promising improvement on the accuracy of the reconstructed data.
Next, the machine learning based filtering framework is extended from single domain modulation schemes to multi-domain. The extended framework is then combined with the spatio-temporally modulated polarimeter from the experimental work to further improve the performance of the multi-domain modulated polarimeter.
Finally, the appendix of the thesis presents a novel non-separable multi-domain modulation design that is able to suppress the systematic error introduced noises. This appendix is closely related to the other work presented in the thesis, but as it represents a different branch of research, it is presented separately to preserve the continuity of the document.