Oscillating Dispersion for Maximal Light-throughput Spectral Imaging

2026-03-16

AI Summary

This work addresses the severe degradation in reconstruction quality under low-light conditions in conventional coded aperture snapshot spectral imaging, which suffers from significant light loss due to occlusion by encoding masks and beam splitters. To overcome this limitation, the authors propose a single-path, near-full-throughput spectral imaging method that alternately captures panchromatic images and dispersed measurements via an axially oscillating dispersive element. A panchromatic-guided dispersive-aware unfolding network (PDAUN) is developed for high-fidelity spectral reconstruction, featuring two key innovations: a dispersive-aware deformable convolution (DADC) module and a fast data fidelity solver based on an FFT-Woodbury preconditioner. Experiments demonstrate state-of-the-art performance on standard benchmarks, with substantial improvements over existing systems particularly in low-illumination scenarios, and physical prototype validation confirms the method’s practical reconstruction capability.

Technology Category

• Computer Vision: Multi-modal Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• spectral imaging
• light throughput
• low illumination
• computational imaging
• reconstruction quality

Innovation

• Oscillating Dispersion
• Full Light-throughput
• PAN-guided Reconstruction
• Dispersion-Aware Deep Unfolding
• Spectral Imaging