Recently a novel Fresnel zone light field spectral imager was developed that provides snapshot spectral imaging using no moving parts or scanning. This system combines a Fresnel zone plate as the primary optic to conduct both imaging and dispersion with a microlens array configured like a plenoptic camera. This encodes spectral information onto the detector array like a traditional plenoptic camera encodes range information, and both systems require post-processing to produce final images. While algorithms will significantly affect final performance, the ability to judge the optical performance of a particular hardware design before post processing is important to set a base line for algorithm comparisons as well as establish how components in the imaging chain impact performance. For remote sensing scenarios we propose a figure of merit based on shifts of the images formed by each microlens of a point source and derive design equations linking this to system parameters. This talk examines how the Fresnel zone plate and microlenses affect the blur of the point source images and how the sampling of the images by the detector array impacts the figure of merit. For remote sensing scenarios an image shift corresponded to a particular source wavelength. The sampling by the detector array added uncertainty to image shift measurements providing a measure of the spectral resolution due to the hardware. The image shift figure of merit describes how spectral information is encoded in the raw data by the hardware and can be used to estimate performance prior to post-processing.
Recent interest in building an imaging system using diffractive optics that can fit on a CubeSat (10 cm x 10 cm x 30 cm) and can correct severe chromatic aberrations inherent to diffractive optics has led to the development of the Fresnel zone light field spectral imaging system (FZLFSI). The FZLFSI is a system that integrates an axial dispersion binary diffractive optic with a light field (plenoptic) camera design that enables snapshot spectral imaging capability. This system suffered from poor resolution and a modified FZLFSI based on full resolution light field rendering has been built and tested. The modified FZLFSI shifts the optical elements to different positions which change the way the light field is encoded on the focal plane array. The new encoding increases the available spatial information at the expense of some spectral information. The system was tested for different internal system parameters, at a range of wavelengths, and the resulting tradeoffs between spatial and spectral performance were studied. The performance of the modified FZLFSI was compared to that of the conventional FZLFSI and optimal internal system parameters identified for different imaging scenarios.
A diffractive plenoptic camera is a novel approach to the traditional plenoptic camera which replaces the main optic with a Fresnel zone plate making the camera sensitive to wavelength instead of range. Algorithms are necessary, however, to reconstruct the image produced by these plenoptic cameras. This paper provides the first quantification of the effectiveness of four different types of post-processing algorithms on a simulated Fresnel zone light field spectral imaging system. The four post-processing algorithms used were standard digital refocusing, 3D deconvolution through a Richardson-Lucy algorithm, a novel Gaussian smoothing algorithm, and a custom-made super resolution algorithm. For the digital refocusing algorithm, the image quality decreased as the wavelength difference from design increased. In comparison, in the Richardson Lucy deconvolution algorithm, the image returned to the same quality as at the design wavelength if enough iterations were used and generally provided results on par with the best near the design wavelength of the Fresnel zone plate and by far the best results far from design at the cost of extensive computation time. The super resolution method, in general, performed better than the standard digital refocusing while the Gaussian smoothing algorithm performed on par with digital refocusing. As a consequence, if time is not a factor, deconvolution should be used in general, while the super resolution method provides faster results if time is an issue. Still, each algorithm outperformed the others in specific cases which allows the best results to be obtained by choosing the algorithm that meets operational requirements and limitations.
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