Compressive spectral imaging using diffractive lenses and multi-spectral sensors with learned reconstruction and joint optimization

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2022-2

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Gündoğan, Utku

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Compressive spectral imaging aims to reconstruct the entire three-dimensional spectral cube from a few measurements, ideally with a snapshot capability. Recently various spectral imaging modalities have been developed by exploiting diffractive lenses. Another line of development in this area is enabled by spectral filter arrays which resulted in multi-spectral sensors. In this thesis, we first review an existing compressive spectral imaging modality with diffractive lenses and analyze its performance using some quantitative metrics. We then study the joint reconstruction and system optimization for this imaging modality using a model-based learned approach with an unrolled deep neural network (DNN). Secondly, we improve on this modality by developing a new spectral imaging system that also exploits a multi-spectral sensor. To reconstruct the spectral cube from its compressive measurements, we design a learned reconstruction method using a similar unrolled network. A fast sparse reconstruction algorithm is also developed and compared with this learned reconstruction method. The performance of the developed imaging technique is illustrated for the visible regime using different design configurations, number of measurements, and signal-to-noise ratios. The results demonstrate that significant performance improvement can be achieved over the existing compressive spectral imaging modality with diffractive lenses, while also enabling snapshot capability with a simpler design.

Subject Keywords

Multi-spectral sensor, Diffractive lens, Spectral imaging, Compressive sensing, Learned reconstruction, Joint optimization, Inverse problems, Image reconstruction

URI

https://hdl.handle.net/11511/96729

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

U. Gündoğan, “Compressive spectral imaging using diffractive lenses and multi-spectral sensors with learned reconstruction and joint optimization,” M.S. - Master of Science, Middle East Technical University, 2022.