Aperture Diffraction for Compact Snapshot Spectral Imaging

• URL: http://arxiv.org/abs/2309.16372v1
• Date: Wed, 27 Sep 2023 16:48:46 GMT
• Title: Aperture Diffraction for Compact Snapshot Spectral Imaging
• Authors: Tao Lv, Hao Ye, Quan Yuan, Zhan Shi, Yibo Wang, Shuming Wang, Xun Cao
• Abstract summary: We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS) A new optical design that each point in the object space is multiplexed to discrete encoding locations on the mosaic filter sensor is introduced. The Cascade Shift-Shuffle Spectral Transformer (CSST) with strong perception of the diffraction degeneration is designed to solve a sparsity-constrained inverse problem.
• Score: 27.321750056840706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras. Then we introduce a new optical design that each point in the object space is multiplexed to discrete encoding locations on the mosaic filter sensor by diffraction-based spatial-spectral projection engineering generated from the orthogonal mask. The orthogonal projection is uniformly accepted to obtain a weakly calibration-dependent data form to enhance modulation robustness. Meanwhile, the Cascade Shift-Shuffle Spectral Transformer (CSST) with strong perception of the diffraction degeneration is designed to solve a sparsity-constrained inverse problem, realizing the volume reconstruction from 2D measurements with Large amount of aliasing. Our system is evaluated by elaborating the imaging optical theory and reconstruction algorithm with demonstrating the experimental imaging under a single exposure. Ultimately, we achieve the sub-super-pixel spatial resolution and high spectral resolution imaging. The code will be available at: https://github.com/Krito-ex/CSST.

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