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Hierarchical Spatial-Frequency Aggregation for Spectral Deconvolution Imaging

arXiv 2511.06751 ↗First posted 10 Nov 2025eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Computational spectral imaging (CSI) achieves real-time hyperspectral imaging through co-designed optics and algorithms, but typical CSI methods suffer from a bulky footprint and limited fidelity. Therefore, Spectral Deconvolution imaging (SDI) methods based on PSF engineering have been proposed to achieve high-fidelity compact CSI design recently. However, the composite convolution-integration operations of SDI render the normal-equation coefficient matrix scene-dependent, which hampers the efficient exploitation of imaging priors and poses challenges for accurate reconstruction. To tackle the inherent data-dependent operators in SDI, we introduce a Hierarchical Spatial-Spectral Aggregation Unfolding Framework (HSFAUF). By decomposing subproblems and projecting them into the frequency domain, HSFAUF transforms nonlinear processes into linear mappings, thereby enabling efficient solutions. Furthermore, to integrate spatial-spectral priors during iterative refinement, we propose a Spatial-Frequency Aggregation Transformer (SFAT), which explicitly aggregates information across spatial and frequency domains. By integrating SFAT into HSFAUF, we develop a Transformer-based deep unfolding method, Hierarchical Spatial-Frequency Aggregation Unfolding Transformer (HSFAUT), to solve the inverse problem of SDI. Systematic simulated and real experiments show that HSFAUT surpasses SOTA methods with cheaper memory and computational costs, while exhibiting optimal performance on different SDI systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 10 Nov 2025.

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18 of 62 references.

References · 18

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  3. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  4. 2021Multi-Stage Progressive Image Restorationcited by 813
  5. 2020Learning Enriched Features for Real Image Restoration and Enhancementcited by 311
  6. 2021Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstructioncited by 110
  7. 2022MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstructioncited by 72
  8. 2022Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstructioncited by 67
  9. 2022HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imagingcited by 66
  10. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  11. 2021Deep Gaussian Scale Mixture Prior for Spectral Compressive Imagingcited by 58
  12. 2020Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter arraycited by 24
6 more
  1. 2022Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imagingcited by 22
  2. 2023Pixel Adaptive Deep Unfolding Transformer for Hyperspectral Image Reconstructioncited by 20
  3. 2023Aperture Diffraction for Compact Snapshot Spectral Imagingcited by 7
  4. 2023Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imagingcited by 7
  5. 2019Compressive Spectral Imaging with Diffractive Lensescited by 3
  6. 2020High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstructioncited by 2

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