光学
快照(计算机存储)
编码孔径
全光谱成像
像素
光谱成像
光子
光子计数
图像分辨率
光谱分辨率
探测器
高光谱成像
波长
物理
材料科学
多光谱图像
噪音(视频)
数据采集
时间分辨率
摄影术
光谱灵敏度
图像处理
相位恢复
成像光谱学
点扩散函数
光谱带
图像质量
医学影像学
光学成像
迭代重建
相位噪声
带宽(计算)
光电探测器
二极管
测距
光谱密度
采样(信号处理)
计算机科学
人工智能
作者
Haoze Song,Yibo Feng,Xilong Dai,Liheng Bian
出处
期刊:Optics Letters
[Optica Publishing Group]
日期:2025-09-15
卷期号:50 (20): 6401-6401
被引量:2
摘要
Capturing spectral information in ultralow-light conditions remains challenging. Here, we propose a single-photon coded aperture snapshot spectral imaging (CASSI) system that incorporates the unique properties of single-photon avalanche diodes (SPAD) with the CASSI framework, efficiently integrating high sensitivity and spectral information. To tackle the challenges of sparse spatial sampling in directly integrated CASSI-SPAD systems and the lack of low-light spectral data and specialized networks, we developed an efficient spectral-spatial model for ultralow-light conditions, with an SPAD-CASSI simulation algorithm utilizing calibrated average photon counts, followed by noise and spectral-spatial encoding. Employing an exposure time of 100 µs at 0.15 lux, we simulated SPAD-CASSI data from 450 to 650 nm in 8 wavelength channels at 64 × 32 pixel resolution and designed a deep learning network for spectral reconstruction. Experimental results on macroscopic scenes, microscopic imaging, and high-speed scenarios show that our approach significantly improves the performance of snapshot spectral imaging in ultralow-light conditions, providing a robust solution for challenging imaging scenarios.
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