mlsanityData to 7 Oct 2026

Aperture Diffraction for Compact Snapshot Spectral Imaging

arXiv 2309.16372 ↗First posted 27 Sep 2023cs.CV eess.IVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 51 references that are in the map. First posted on arXiv 27 Sep 2023.

Builds on

16 of 51 references.

References · 16

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4 more
  1. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  2. 2021Deep Gaussian Scale Mixture Prior for Spectral Compressive Imagingcited by 58
  3. 2020GAP-net for Snapshot Compressive Imagingcited by 54
  4. 2022When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanismcited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 4.

7 papers.