基于深度学习的滤光片型高光谱成像技术  

Filtering Hyperspectral Imaging Technology Based on Deep Learning

作　　者：林学利 王子林 邹艳霞 刘豪 郝然 金尚忠 Lin Xueli;Wang Zilin;Zou Yanxia;Liu Hao;Hao Ran;Jin Shangzhong(College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310018,Zhejiang,China;Key Laboratory of Zhejiang Province on Modern Measurement Technology and Instruments,Hangzhou 310018,Zhejiang,China)

机构地区：[1]中国计量大学光学与电子科技学院,浙江杭州310018 [2]浙江省现代计量测试技术及仪器重点实验室,浙江杭州310018

出　　处：《激光与光电子学进展》2023年第10期460-468,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61975182,61575174);浙江省重大科技计划项目(2020C03095)。

摘　　要：相较于传统快照式高光谱成像技术,基于深度学习的滤光片型高光谱成像技术仅使用深度学习和极少的滤光片进行光谱采样,便能重建高光谱,且滤光片直接与图像传感器集成,具有结构简单、成像速度快等优点。但现有的研究大多直接以原高光谱成像仪拍摄的图像为数据集,而未对数据集进行预处理,忽略了原高光谱成像仪对数据集的影响。因此,通过对原高光谱成像仪成像原理进行研究来对数据集进行预处理,把高光谱图像转换为辐射功率谱,从而消除原高光谱成像仪的影响,增强了模型鲁棒性。另外,鉴于滤光片存在光谱响应函数平滑性差而难以加工的问题,将平滑性约束纳入误差函数的设计中,使优化所得的滤光片具有平滑的光谱响应函数且易于加工。Deep learningbased filtering hyperspectral imaging technique can reconstruct hyperspectral images,which only requires deep learning and a few filters for spectral sampling.The filters are also directly integrated with the image sensor,resulting in a simple structure and quick imaging compared to typical snapshot hyperspectral imaging technology.However,most existing studies directly use the images taken by the original hyperspectral imager as the dataset without preprocessing,ignoring the impact of the original hyperspectral imager on the dataset.In this study,the dataset was preprocessed by examining the imaging mechanism of the original hyperspectral camera,which means that the hyperspectral image was converted into a radiative power spectrum to remove the effect of the original hyperspectral camera,resulting in a more robust model than in previous studies.Furthermore,because the spectral response function has poor smoothness,the filters are difficult to produce;thus,the smoothness constraint is incorporated into the error function to create a smooth and easytoproduce filter.

关 键 词：光谱学 高光谱成像 计算光谱学 光学逆向设计 

分 类 号：TG115.339[金属学及工艺—物理冶金]