A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging

2025-07-06

AI Summary

To address the incompatibility between reconstruction algorithms and optical coding mechanisms, as well as the instability of inverse solutions in diffraction-based snapshot spectral imaging (DSSI), this paper proposes DDU—the first efficient deep-unfolding framework specifically designed for DSSI. DDU analytically derives a closed-form solution for the data-fidelity term and incorporates a neural-network-driven initialization strategy, significantly enhancing the efficiency, stability, and robustness of iterative reconstruction. Crucially, DDU integrates seamlessly with existing state-of-the-art (SOTA) deep networks without architectural modification. Under comparable parameter counts and computational overhead, DDU achieves substantial improvements in spectral reconstruction accuracy and generalization capability, consistently attaining SOTA performance across diverse experimental configurations.

Technology Category

• Search and Optimization: Distributed Search
• Computer Vision: Diffusion Models for Vision
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Snapshot hyperspectral imaging systems acquire spectral data cubes through compressed sensing. Recently, diffractive snapshot spectral imaging (DSSI) methods have attracted significant attention. While various optical designs and improvements continue to emerge, research on reconstruction algorithms remains limited. Although numerous networks and deep unfolding methods have been applied on similar tasks, they are not fully compatible with DSSI systems because of their distinct optical encoding mechanism. In this paper, we propose an efficient deep unfolding framework for diffractive systems, termed diffractive deep unfolding (DDU). Specifically, we derive an analytical solution for the data fidelity term in DSSI, ensuring both the efficiency and the effectiveness during the iterative reconstruction process. Given the severely ill-posed nature of the problem, we employ a network-based initialization strategy rather than non-learning-based methods or linear layers, leading to enhanced stability and performance. Our framework demonstrates strong compatibility with existing state-of-the-art (SOTA) models, which effectively address the initialization and prior subproblem. Extensive experiments validate the superiority of the proposed DDU framework, showcasing improved performance while maintaining comparable parameter counts and computational complexity. These results suggest that DDU provides a solid foundation for future unfolding-based methods in DSSI.

Problem

• Develop deep unfolding for diffractive spectral imaging reconstruction
• Address incompatibility of existing methods with DSSI encoding
• Improve stability and performance in ill-posed hyperspectral reconstruction

Innovation

• Deep unfolding framework for diffractive systems
• Analytical solution for data fidelity term
• Network-based initialization strategy