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Hyperspectral phase imaging based on denoising in complex-valued eigensubspace
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DOI：10.1016/j.optlaseng.2019.105973.png)
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Abstract
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A novel algorithm for reconstruction of hyperspectral 3D complex domain images (phase/amplitude) from noisy complex domain observations has been developed and studied. This algorithm starts from the SVD (singular value decomposition) analysis of the observed complex-valued data and looks for the optimal low dimension eigenspace. These eigenspace images are processed based on special non-local block-matching complex domain filters. The accuracy and quantitative advantage of the new algorithm for phase and amplitude imaging are demonstrated in simulation tests and in processing of the experimental data. It is shown that the algorithm is effective and provides reliable results even for highly noisy data.
Keywords:
Hyperspectral imaging
Phase imaging
Singular value decomposition
Sparse representation
Noise filtering
Noise in imaging systems
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