基于块稀疏贝叶斯模型的鬼成像重构算法  

Ghost Imaging Reconstruction Algorithm Based on Block Sparse Bayesian Model

作　　者：吴学林[1] 朱荣 郭迎[3] WU Xue-lin;ZHU Rong;GUO Ying(School of Internet of Things Engineering,Wuxi Taihu University,Wuxi,Jiangsu 214000,China;School of Computer Science,Qufu Normal University,Rizhao,Shandong 276826,China;School of Automation,Central South University,Changsha 410083,China)

机构地区：[1]无锡太湖学院物联网工程学院,江苏无锡214000 [2]曲阜师范大学计算机学院,山东日照276826 [3]中南大学自动化学院,长沙410083

出　　处：《计算机科学》2020年第S02期188-191,214,共5页Computer Science

基　　金：国家自然科学基金(61876407);江苏省物联网应用技术重点建设实验室(19WXWL05,18WXWL01)。

摘　　要：传统的相机系统使用物体透射或从物体反向散射的光在胶片或焦平面探测器阵列上形成图像,鬼成像系统则使用分离的光场之间的空间相关性来获得图像而且无需记录图像本身,在遥感、医学和显微成像方面具有巨大的应用潜力。然而传统的鬼成像系统存在大尺寸图像重构存储要求高难以实现的问题。针对此问题,本文提出了一种基于块稀疏贝叶斯模型的鬼成像重构算法。该算法首先将一个大尺寸的目标图像等分成若干个小尺寸图像块,然后再利用贝叶斯学习模型对每一个小图像块进行压缩感知重构求解,最后通过合并每一个小图像块的重构结果,得到最终的大目标重构图像。仿真实验结果显示,基于块稀疏贝叶斯的鬼成像重构算法可以明显提升图像重构速度及重构质量,并且在日常条件下也可以快速有效地重构大尺寸目标图像。Conventional camera systems use light transmitted or backscattered from an object to form an image on a film or focal plane detector array.Ghost imaging systems utilize the spatial correlation between separated light fields to obtain images without recording the images themselves,and have great application potential in remote sensing,medical,and microscopic imaging.A ghost imaging reconstruction algorithm based on block sparse Bayesian model is proposed to improve the problem that large-scale image reconstruction storage is difficult to achieve in traditional ghost imaging systems.This algorithm divides a large-size target image into several small-sized image blocks of the same size.Based on the Bayesian learning model,each image block is subjected to compressed sensing reconstruction.Subsequently,the reconstruction result of each image block is merged,resulting in the final target reconstructed image.The simulation results show that the image quality of the reconstructed image can be improved from the block sparse Bayesian ghost imaging reconstruction algorithm,and the large-size target image can be reconstructed effectively under the traditional computer configuration for practical implementations.

关 键 词：块稀疏贝叶斯模型 压缩感知 鬼成像 图像重构 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]