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Compact Snapshot Spectral Imaging with Calibration-Free Aperture Diffraction

arXiv 2608.29230 ↗First posted 29 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

Snapshot Spectral Imaging (SSI) provides high-dimensional temporal-spatial-spectral observation to uncover intrinsic physical characteristics. However, its complex system and repetitive calibration requirements hinder edge applications. Here, we propose a compact, cost-effective, calibration-free SSI method, Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of a diffractive lens with a binary mask and a Bayer-filtered sensor, requiring no additional physical footprint compared to standard RGB cameras. ADIS disperses and multiplexes wavelengths, mapping energy to distinct sensor locations, enabling full-resolution recovery from superpixel-level encodings. ADIS directly leverages theoretically computed PSFs to enable calibration-free spectral reconstruction, while tolerating lens-dependent variations across different optical configurations and bridging the gap between simulation and reality. To achieve SSI by solving a sparsely-constrained inverse problem, we introduce the Orthogonal Diffraction-Aware Unfolding Framework (ODAUF) with Voxel Shift Transformer (VST) for improved orthogonal diffraction perception. Integrating VST into ODAUF forms the efficient Orthogonal Diffraction-Aware Unfolding Voxel Shift Transformer (ODAUVST), delivering excellent recovery and reduced parameters. By elaborating on theory, systematic and comprehensive comparing, and demonstrating real SSI results, we validate the superiority of ADIS, achieving calibration-free full-resolution SSI within a commercial camera footprint.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 51 references that are in the map. First posted on arXiv 29 Aug 2026.

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12 of 51 references.

References · 12

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
  3. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  4. 2021Multi-Stage Progressive Image Restorationcited by 816
  5. 2020Learning Enriched Features for Real Image Restoration and Enhancementcited by 312
  6. 2021Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstructioncited by 110
  7. 2018Denoising Prior Driven Deep Neural Network for Image Restorationcited by 80
  8. 2022MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstructioncited by 72
  9. 2022Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstructioncited by 67
  10. 2022HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imagingcited by 66
  11. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  12. 2023Aperture Diffraction for Compact Snapshot Spectral Imagingcited by 7

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