基于深度学习的非视域成像  

Non-Line-of-Sight Imaging Through Deep Learning

作　　者：于亭义 乔木 刘红林[1] 韩申生[1] Yu Tingyi;Qiao Mu;Liu Honglin;Han Shensheng(Key Laboratory for Quantum Optics,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院上海光学精密机械研究所量子光学重点实验室,上海201800 [2]中国科学院大学材料与光电研究中心,北京100049

出　　处：《光学学报》2019年第7期79-85,共7页Acta Optica Sinica

基　　金：国家重点研发计划(2016YFC0100600)

摘　　要：针对非相干光照明下的非视域成像问题,提出一种基于深度学习的解决方法。结合计算机视觉领域中经典的语义分割及残差模型,构造一种URNet网络结构,并改进了经典瓶颈层结构。实验结果表明,改进的网络可以恢复更多的图像细节,并具有一定泛化性,相比于基于非相干光照明的散斑自相关成像技术,该网络恢复性能有较大提升。Aiming at the problem of non-line-of-sight imaging under incoherent illumination, we propose a solution based on deep learning. Combining the classical semantic segmentation and residual model in the field of computer vision, a URNet network structure is constructed and the classical bottleneck layer structure is improved. The experimental results show that the improved model has more details of recovery images and generalization ability. Compared with speckle autocorrelation imaging method under incoherent illumination, the recovery performance of this method is greatly improved.

关 键 词：成像系统 非视域成像 深度学习 语义分割 残差模型 

分 类 号：O438[机械工程—光学工程]