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 threshold shrinkage method to optimize alternately.After every fifteen iterations,perform a principal component analysis on the classified three-dimensional spectral patches to obtain a new dictionary,and continue to repeat the iterative process.Through multiple repetitions,the final objective function converges,and the reconstructed spectral image can be obtained.We have done simulation experiments on the public spectral image dataset,and the experimental results show that our method has smaller spatial and spectral dimensions errors than other methods.We conducted simulation experiments on public spectral datasets,and the results show that our method has smaller errors in both spatial and spectral dimensions.From the perspective of spatial dimension,the proposed method can retain more details.From the spectral dimension,the method has smaller error and smaller error fluctuation in almost all wavebands than other methods.In addition,we compare the RGB auxiliary dictionary learning and similarity estimation method proposed in this paper with the common intermediate result dictionary learning and similarity estimation methods,the RGB auxiliary reconstruction method saves nearly half of the time while maintaining the same reconstruction quality.Finally,we set up an imaging system to do experiments on real data,and took images with filters for reference.The experiments show that our method can also obtain the best reconstruction quality on real data,which is most similar to the images obtained with filters.We also analyzed the influence of some factors,such as sampling step size and patch size,and selected the most appropriate parameter settings through a large number of experiments.Experiments on simulation data and real data show that our reconstruction model can greatly improve the reconstruction quality of spectral images in spatial and spectral dimensions,and the RGB observation assisted reconstruction method can effectively reduce the reconstruction time.
作者
朱骏捷
赵巨峰
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)
出处
《光子学报》
EI
CAS
CSCD
北大核心
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)。
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
Spectral imaging
Compressed sensing
Coded aperture
Non-local similarity
Sparsity
Dual camera
