基于非局部自相似性的双相机压缩光谱图像重建算法  

Dual-camera Compressed Spectral Image Reconstruction Algorithm Based on Non-local Self-similarity

作　　者：朱骏捷 赵巨峰[1,2] 田海军 崔光茫 石振 ZHU Junjie;ZHAO Jufeng;TIAN Haijun;CUI Guangmang;SHI Zhen(Institute of Carbon Neutrality and New Energy,Hangzhou Dianzi University,Hangzhou 310018,China;School of Electronics and Information,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学碳中和新能源研究院,杭州310018 [2]杭州电子科技大学电子信息学院,杭州310018

出　　处：《光子学报》2023年第1期27-37,共11页Acta Photonica Sinica

基　　金：浙江省自然科学基金(Nos.LY22F050002,LGF20F050003,LQ20F030011);浙江省科协“育才工程”项目(No.SKX201901)。

摘　　要：针对压缩光谱成像的图像重建问题,提出了一种基于非局部稀疏表示与双相机系统的压缩光谱重建方法。首先,利用RGB观测来构建一种三维图像块,使用K均值聚类对图像块进行分类,并以聚类结果来指导目标高光谱图像的光谱块分类,通过主成分分析获取每个簇的特征用来稀疏表示其他光谱块。然后用构建的三维图像块估计目标光谱图像非局部相似性,并构建目标函数。最后,通过迭代收缩算法与共轭梯度下降法来交替优化目标函数完成重建。仿真和实拍结果表明,所提方法能大幅提升重建质量与精度,在空间和光谱维度上重建误差更小,RGB观测辅助字典学习与相似块估计的方法能有效提升双相机系统的计算效率。Coded aperture spectral imaging is a snapshot spectral imaging method,but it usually has the problems of large reconstruction error and high reconstruction computational complexity.To solve this problem,this paper proposes a compressed spectral reconstruction method based on non-local sparse representation and dual-camera system.First,a dual camera system is used to obtain the spectral and spatial data of the target.This dual camera system has two branches,the light is divided into two paths through a spectroscope,half enters the coded aperture spectral imaging system to obtain encoded images,and the other half is received by an RGB camera to obtain RGB images.The RGB observation image is used to construct 3D image patches,and k-means clustering is used to classify these 3D image patches.Then we propose a method to estimate the non-local similarity of target spectral image by RGB observation.The clustering and similarity estimation results of 3D image patches are used to guide the classification and similarity estimation of target spectral images.Divide the initialized target spectral image into a series of three-dimensional spectral patches,and classify the spectral patches based on the previous clustering results.Perform principal component analysis on each cluster,obtain the common features between different patches of the target spectral image,and use them to sparsely represent other spectral patches.For each patch,the sparse representation coefficients of the current patches are estimated by the weighted sum of sparse representation coefficients of nonlocal similar patches,and the weighted coefficients are calculated from the 3D image patches constructed by RGB observation.In order to improve the reconstruction quality,we set adaptive regularization parameters for sparse representation coefficients.We transform these operations into a variational optimization model,and then adopt an alternative optimization scheme to solve the objective function.We use conjugate gradient descent method and iterative threshol

关 键 词：光谱成像 压缩感知 编码孔径 非局部自相似性 稀疏性 双相机 

分 类 号：O433.1[机械工程—光学工程]