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.
Abstract (translated)
高光谱相机在低光子环境下面临着空间分辨率、光谱分辨率和时间分辨率之间的严峻权衡。计算成像系统通过压缩感知突破了这些限制，但需要复杂的光学设备和/或大量的计算资源。我们提出了“从脱焦中恢复光谱（SfD）”方法，这是一种色差焦点扫描技术，仅使用一套现成的光学元件及不到1秒的计算时间即可获取最先进的高光谱图像。 我们的相机采用两片透镜和一个灰度传感器来保留几乎所有的入射光线，并将它们存储在一个具有色差的焦点堆栈中。我们基于物理原理的迭代算法可以高效地对模糊的灰度焦点堆栈进行解混、反卷积以及去噪，从而生成清晰的光谱图像。 这种方法在光子效率、光学设计简洁性及物理模型应用方面的结合使其成为快速、紧凑且易于理解的高光谱成像解决方案。
URL
https://arxiv.org/abs/2503.20184
