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Oscillating Dispersion for Maximal Light-throughput Spectral Imaging

Jiuyun Zhang, Zhan Shi, Linsen Chen, Xun Cao

Abstract

Existing computational spectral imaging systems typically rely on coded aperture and beam splitters that block a substantial fraction of incident light, degrading reconstruction quality under light-starved conditions. To address this limitation, we develop the Oscillating Dispersion Imaging Spectrometer (ODIS), which for the first time achieves near-full light throughput by axially translating a disperser between the conjugate image plane and a defocused position, sequentially capturing a panchromatic (PAN) image and a dispersed measurement along a single optical path. We further propose a PAN-guided Dispersion-Aware Deep Unfolding Network (PDAUN) that recovers high-fidelity spectral information from maskless dispersion under PAN structural guidance. Its data-fidelity step derives an FFT-Woodbury preconditioned solver by exploiting the cyclic-convolution property of the ODIS forward model, while a Dispersion-Aware Deformable Convolution module (DADC) corrects sub-pixel spectral misalignment using PAN features. Experiments show state-of-the-art performance on standard benchmarks, and cross-system comparisons confirm that ODIS yields decisive gains under low illumination. High-fidelity reconstruction is validated on a physical prototype.

Oscillating Dispersion for Maximal Light-throughput Spectral Imaging

Abstract

Existing computational spectral imaging systems typically rely on coded aperture and beam splitters that block a substantial fraction of incident light, degrading reconstruction quality under light-starved conditions. To address this limitation, we develop the Oscillating Dispersion Imaging Spectrometer (ODIS), which for the first time achieves near-full light throughput by axially translating a disperser between the conjugate image plane and a defocused position, sequentially capturing a panchromatic (PAN) image and a dispersed measurement along a single optical path. We further propose a PAN-guided Dispersion-Aware Deep Unfolding Network (PDAUN) that recovers high-fidelity spectral information from maskless dispersion under PAN structural guidance. Its data-fidelity step derives an FFT-Woodbury preconditioned solver by exploiting the cyclic-convolution property of the ODIS forward model, while a Dispersion-Aware Deformable Convolution module (DADC) corrects sub-pixel spectral misalignment using PAN features. Experiments show state-of-the-art performance on standard benchmarks, and cross-system comparisons confirm that ODIS yields decisive gains under low illumination. High-fidelity reconstruction is validated on a physical prototype.
Paper Structure (14 sections, 12 equations, 5 figures, 4 tables)

This paper contains 14 sections, 12 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. ODIS: System Design
  4. PDAUN: Algorithm Design
  5. ADMM Deep Unfolding Framework
  6. FFT-Woodbury Preconditioned CG for the x-step
  7. Dispersion-Aware Deformable Convolution
  8. Architecture Overview
  9. Experiments
  10. Cross-System Comparison
  11. Algorithm Comparison
  12. Real-World Experiment
  13. Ablation Study
  14. Conclusion

Figures (5)

  • Figure 1: Depiction of different computational spectral imaging systems. From left to right: coded-aperture systems modulate light with an amplitude mask; spectral-filter-based and diffraction-based systems encode spectral information via wavelength-selective filters or diffractive elements, respectively; ODIS encodes spectra through axial prism translation with a grayscale camera.
  • Figure 2: Optical layout of ODIS. (a) With the Amici prism at the image-conjugate plane, all wavelengths from a scene point converge to a common sensor location, producing a sharp PAN image. (b) Translating the prism along the optical axis to the defocused plane causes each wavelength to focus at a distinct lateral position, yielding a spectrally dispersed measurement.
  • Figure 3: Overview of PDAUN. (a) PDAUN architecture with $K$ unfolding stages. (b) Denoiser with three-layer U-shaped structure. (c) FFT-Woodbury preconditioned conjugate-gradient solver. (d) Dispersion-Aware Deformable Convolution (DADC) module.
  • Figure 4: Cross-system simulation HSI reconstruction comparison under low illumination ($9$ lux). The bottom-left illustrates normalized spectrum curves at the position marked by pink triangle in the PAN image. The right shows reconstruction results at spectral bands 476.5nm, 536.5nm, 584.3nm, 648.1nm. Zoom in for a better view.
  • Figure 5: Real-world reconstruction on the ODIS prototype. (a) Raw measurements under standard illumination ($\approx$774 lux); spectral curves at the position marked by a red sun are compared with spectrometer references. (b) Reconstructed 21-channel HSI and (c) its pseudo-RGB rendering. (d), (e) Corresponding results under low-light conditions ($\approx$6.7 lux). (f) Photograph of the ODIS prototype.