Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack

2025-03-26

AI Summary

Addressing the fundamental trade-off among spatial, spectral, and temporal resolution in low-photon high-spectral imaging, this paper proposes a computationally efficient, optically minimalist, and physically interpretable computational imaging framework. We construct a defocused chromatic focal stack using only two off-the-shelf lenses and a single grayscale sensor, enabling full-photon-throughput raw data acquisition. We introduce the first focal-stack inversion framework grounded in a physics-based chromatic aberration model, integrating joint unmixing, deconvolution, and denoising within an iterative optimization scheme. The method eliminates the need for filter wheels or mechanical scanning, resulting in low hardware cost and straightforward system integration. Reconstruction time is under one second, achieving state-of-the-art hyperspectral image quality while simultaneously optimizing photon efficiency, optical simplicity, and algorithmic interpretability.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web crawling and indexing
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in an inherently low-photon regime. Computational imaging systems break through these trade-offs with compressive sensing, but require complex optics and/or extensive compute. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that recovers state-of-the-art hyperspectral images with a small system of off-the-shelf optics and<1 second of compute. Our camera uses two lenses and a grayscale sensor to preserve nearly all incident light in a chromatically-aberrated focal stack. Our physics-based iterative algorithm efficiently demixes, deconvolves, and denoises the blurry grayscale focal stack into a sharp spectral image. The combination of photon efficiency, optical simplicity, and physical modeling makes SfD a promising solution for fast, compact, interpretable hyperspectral imaging.

Problem

• Overcoming trade-offs in hyperspectral imaging resolution
• Reducing complexity in computational imaging systems
• Achieving fast, efficient hyperspectral image recovery

Innovation

• Chromatic focal sweep for spectral imaging
• Two-lens grayscale sensor system
• Physics-based iterative demixing algorithm