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cs.LG updates on arXiv.org

Memory-Guided Trust-Region Bayesian Optimization (MG-TuRBO) for High Dimensions EngageTriBoost: Predictive Modeling of User Engagement in Digital Mental Health Intervention Using Explainable Machine Learning Reservoir observer enhanced with residual calibration and attention mechanism Efficient RL Training for LLMs with Experience Replay Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning Adversarial Sensor Errors for Safe and Robust Wind Turbine Fleet Control IKKA: Inversion Classification via Critical Anomalies for Robust Visual Servoing Adaptive Simulation Experiment for LLM Policy Optimization EvoLen: Evolution-Guided Tokenization for DNA Language Model Smartwatch-Based Sitting Time Estimation in Real-World Office Settings Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis Loom: A Scalable Analytical Neural Computer Architecture Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance Finite-Sample Analysis of Nonlinear Independent Component Analysis:Sample Complexity and Identifiability Bounds How does Chain of Thought decompose complex tasks? 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The Marginal Importance of Distortions and Alignment in CASSI systems
2025-01-22 · via cs.LG updates on arXiv.org

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.