Computational spectral imaging techniques using diffractive lenses and compressive sensing

Kar, Oğuzhan Fatih
Spectral imaging is a fundamental diagnostic technique in physical sciences with application in diverse fields such as physics, chemistry, biology, medicine, astronomy, and remote sensing. In this thesis, we first present a modified version of a high-resolution computational spectral imaging modality and develop a fast sparse recovery method to solve the associated large-scale inverse problems. This technique uses a diffractive lens called photon sieve for dispersing the optical field. We then extend this technique to obtain super-resolution using an additional coded aperture to spatially modulate the field before dispersion. We also demonstrate the capability of the system in a compressive setting where the entire three-dimensional spectral cube is reconstructed from highly compressed measurements through sparse recovery. In all of the imaging modalities, we numerically illustrate the performance for various settings and obtain promising results. Lastly, we provide a detailed analysis on the spatio-spectral resolution and optimization of the system from both analytical and numerical aspects.


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Spectral imaging, the sensing of spatial information as a function of wavelength, is a widely used diagnostic technique in diverse fields such as physics, chemistry, biology, medicine, astronomy, and remote sensing. In this paper, we present a novel computational imaging modality that enables high-resolution spectral imaging by distributing the imaging task between a photon sieve system and a computer. The photon sieve system, coupled with a moving detector, provides measurements from multiple planes. Then ...
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Citation Formats
O. F. Kar, “Computational spectral imaging techniques using diffractive lenses and compressive sensing,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.