The Fresnel Zone Light Field Spectral Imager

This thesis provides a computational model and the first experimental demonstration of a Fresnel zone light field spectral imaging (FZLFSI) system. This type of system couples an axial dispersion binary diffractive optic with light field (plenoptic) camera designs providing a snapshot spectral imaging capability. A computational model of the system was developed based on wave optics methods using Fresnel propagation. It was validated experimentally and provides excellent demonstration of system capabilities. The experimentally demonstrated system was able to synthetically refocus monochromatic images across greater than a 100nm bandwidth. Furthermore, the demonstrated system was modeled to have a full range of approximately 400 to 800nm with close to a 15nm spectral sampling interval. While images of multiple diffraction orders were observed in the measured light fields, they did not degrade the system\u27s performance. Experimental demonstration also showed the capability to resolve between and process two different spectral signatures from a single snapshot. For future FZLFSI designs, the study noted there is a fundamental design trade-off, where improved spectral and spatial resolution reduces the spectral range of the system

Computational Spectral Imaging: A Contemporary Overview

Spectral imaging collects and processes information along spatial and spectral coordinates quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral images (SIs) allow identifying objects, crops, and materials in the scene through their spectral behavior. Since most spectral optical systems can only employ 1D or maximum 2D sensors, it is challenging to directly acquire the 3D information from available commercial sensors. As an alternative, computational spectral imaging (CSI) has emerged as a sensing tool where the 3D data can be obtained using 2D encoded projections. Then, a computational recovery process must be employed to retrieve the SI. CSI enables the development of snapshot optical systems that reduce acquisition time and provide low computational storage costs compared to conventional scanning systems. Recent advances in deep learning (DL) have allowed the design of data-driven CSI to improve the SI reconstruction or, even more, perform high-level tasks such as classification, unmixing, or anomaly detection directly from 2D encoded projections. This work summarises the advances in CSI, starting with SI and its relevance; continuing with the most relevant compressive spectral optical systems. Then, CSI with DL will be introduced, and the recent advances in combining the physical optical design with computational DL algorithms to solve high-level tasks

One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense