计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 188-191.doi: 10.11896/jsjkx.200200058
吴学林1, 朱荣1,2, 郭迎3
WU Xue-lin1, ZHU Rong1,2, GUO Ying3
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。
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