高光谱成像
全色胶片
人工智能
全光谱成像
计算机视觉
计算机科学
快照(计算机存储)
编码孔径
数据立方体
光谱成像
迭代重建
图像分辨率
图像处理
光学
图像(数学)
物理
探测器
电信
程序设计语言
操作系统
作者
Haijun Tian,Jufeng Zhao,Junjie Zhu,Xuanji Tang,Guangmang Cui,Changlun Hou
出处
期刊:Applied Optics
[The Optical Society]
日期:2023-05-03
卷期号:62 (14): 3649-3649
摘要
Coded aperture snapshot spectral imaging (CASSI) aims to capture the high-dimensional (usually 3D) data cube using a 2D sensor in a single snapshot. Due to the ill-posed snapshot, the reconstruction results are not ideal. One feasible solution is to utilize additional information such as the panchromatic measurement in CASSI. In this paper, we propose a dual-camera hyperspectral reconstruction method based on the deep image prior (DIP) and a guided filter. In particular, the panchromatic measurements are used to estimate spatial detail, and spectral details are provided using CASSI measurements. These measurements are used as a priori learning by the self-supervised network. Using iteration combined with DIP, the hyperspectral reconstruction is continuously updated iteratively. Finally, the panchromatic measurement is used as the guidance image, and the reconstruction result is optimized by guide filtering. A large number of experimental results demonstrate that our method without training data can reconstruct spectral data with both high spectral accuracy and spatial resolution.
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