Deblurring of Hyperspectral Images via Hyperspectral Unmixing

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Mahdavi Javid, Alireza | 2016

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 48712 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Amini, Arash
Abstract:
In this thesis, we propose a new compressed-sensing-based algorithm for unmixing of hyperspectral data, and show that the reconstruction quality could be significantly improved. In addition, we illustrate that by utilizing this approach, we can achieve an approximate estimation of the Point Spread Function (PSF) of the hyperspectral images. In this way, we first assume that the PSF belongs to a specific family of functions, such as Gaussian, then, by sweeping the parameters of the assumed PSF, we obtain the abundance coefficient matrix of the reconstructed image. Now, by choosing the sparsest coefficient matrix as the best one, we estimate the corresponding PSF. Then, we further investigate the performance of our proposed algorithm for two cases of Gaussian and Moffat PSF kernels and show that the Moffat function results in better reconstruction qualities. Finally, we present the results of testing the algorithm on real data cubes, and list the pros and cons of our proposed algorithm along with some ideas for future works
Keywords:
Compressive Sensing ; Hyperspectral Images ; Hyperspectral Unmixing ; Point Spread Function ; Moffat Function

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