CASSI系统中畸变与对准的边缘重要性
The Marginal Importance of Distortions and Alignment in CASSI systems
- LAAS-CNRS（法国国家科学研究中心LAAS实验室）
- IRAP（法国天体物理与行星学研究所）
- Université de Toulouse（图卢兹大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出可微光线追踪模型模拟CASSI系统畸变与像差，证明正确建模下畸变和错位对重建质量影响微小，从而放宽约束以促进新型成像仪器开发。
AI中文摘要：
本文介绍了一种基于可微光线追踪的模型，该模型结合了像差和畸变，利用编码孔径光谱快照成像仪（CASSI）渲染逼真的编码高光谱采集。CASSI系统现在可以优化，以同时满足多个光学设计约束和处理约束。我们设计并建模了四个具有不同程度光学像差的可比较CASSI系统。每个系统生成的渲染高光谱采集与五种最先进的高光谱立方体重建过程相结合。这些重建过程包含从每个系统的传播模型创建的映射函数，以在重建过程中考虑畸变和像差。我们的分析表明，如果正确建模，系统的几何畸变和色散元件错位的影响对重建高光谱数据立方体的整体质量具有边缘重要性。因此，放宽对测量一致性和场景保真度的传统约束，能够通过应用于采集设计或处理性能指标，开发新型成像仪器。通过提供设计、仿真和评估的完整框架，这项工作有助于优化和探索新的CASSI系统，并更广泛地服务于计算成像社区。
英文摘要：
This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic coded hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI). CASSI systems can now be optimized in order to fulfill simultaneously several optical design constraints as well as processing constraints. Four comparable CASSI systems with varying degree of optical aberrations have been designed and modeled. The resulting rendered hyperspectral acquisitions from each of these systems are combined with five state-of-the-art hyperspectral cube reconstruction processes. These reconstruction processes encompass a mapping function created from each system's propagation model to account for distortions and aberrations during the reconstruction process. Our analyses show that if properly modeled, the effects of geometric distortions of the system and misalignments of the dispersive elements have a marginal impact on the overall quality of the reconstructed hyperspectral data cubes. Therefore, relaxing traditional constraints on measurement conformity and fidelity to the scene enables the development of novel imaging instruments, guided by performance metrics applied to the design or the processing of acquisitions. By providing a complete framework for design, simulation and evaluation, this work contributes to the optimization and exploration of new CASSI systems, and more generally to the computational imaging community.
