Abstract
Spectral imaging technology has long-faced fundamental challenges in balancing spectral, spatial, and temporal reso- lutions. While compressive sensing-based Coded Aperture Snapshot Spectral Imaging (CASSI) mitigates this trade-off through optical encoding, high compression ratios result in ill-posed reconstruction problems. Traditional model-based methods exhibit limited performance due to reliance on handcrafted inherent image priors, while deep learning approaches are constrained by their black-box nature, which compromises physical interpretability. To address these limitations, we propose a dual-camera CASSI reconstruction framework that integrates total variation (TV) subgradient theory. By es- tablishing an end-to-end SD-CASSI mathematical model, we reduce the computational complexity of solving the inverse problem and provide a mathematically well-founded framework for analyzing multi-camera systems. A dynamic regular- ization strategy is introduced, incorporating normalized gradient constraints from RGB/panchromatic-derived reference images, which constructs a TV subgradient similarity function with strict convex optimization guarantees. Leveraging spatial priors from auxiliary cameras, an adaptive reference generation and updating mechanism is designed to provide subgradient guidance. Experimental results demonstrate that the proposed method effectively preserves spatial-spectral structural consistency. The theoretical framework establishes an interpretable mathematical foundation for computational spectral imaging, demonstrating robust performance across diverse reconstruction scenarios. The source code is available at this https URL.
Abstract (translated)
光谱成像技术长期以来在平衡光谱、空间和时间分辨率方面面临基本挑战。虽然基于压缩感知的编码孔径快照光谱成像（CASSI）通过光学编码缓解了这种权衡，但高压缩比导致重建问题变得不适定。传统模型驱动的方法由于依赖于手工设计的先验图像特征表现出有限性能，而深度学习方法则受限于其黑箱特性，从而牺牲了物理可解释性。为了解决这些局限性，我们提出了一种双摄像头CASSI重建框架，集成了全变差（TV）次梯度理论。通过建立端到端SD-CASSI数学模型，我们减少了求解逆问题的计算复杂度，并提供了一个分析多相机系统的数学基础框架。引入了动态正则化策略，该策略结合来自RGB/全色图像导出参考图象的归一化梯度约束，构建具有严格凸优化保证的TV次梯度相似函数。利用辅助摄像头的空间先验信息，设计了一种自适应参考生成和更新机制以提供次梯度指导。实验结果表明所提出的方法在保持空间-光谱结构一致性方面效果显著。理论框架为计算光谱成像建立了可解释的数学基础，并且在各种重建场景中表现出强大的性能。源代码可在该链接获取：[这里插入链接]。
URL
https://arxiv.org/abs/2509.10897
