可抑制大气湍流影响的深度学习计算鬼成像

Deep Learning Based Computational Ghost Imaging Alleviating the Effects of Atmospheric Turbulence

计算鬼成像利用光场的二阶相干性进行成像。当探测光经历未知扰动(如大气湍流)时,无法通过计算获得实际到达物体的光场,此时按无扰动的计算光场进行图像重构时会产生图像模糊。提出一种基于深度学习的图像分类-复原方法,用于消除大气湍流对计算鬼成像的影响。该方法的基本思想是先用基于卷积神经网络的分类网络对图像按模糊程度进行分类;然后对每个分类下的图像,分别采用基于生成对抗网络的复原网络进行复原。通过仿真建立了包含大气湍流的压缩感知计算鬼成像模型,得到了不同强度的大气湍流引起的模糊图像,给出了采用深度学习方法对模糊图像进行分类和复原的结果。仿真表明,采用分类-复原网络可以有效提升计算鬼成像的像质,复原后的图像结构相似度和峰值信噪比均有明显提升,并且该网络对不同类型目标具有一定的泛化能力。

Computational ghost imaging uses the second-order coherence of light fields to reconstruct images.In the case of an unknown disturbance(like atmospheric turbulence)to the probe light,the actual light field reaching the object cannot be calculated,and the images will become blurred if they are reconstructed according to the calculated light field without disturbance.In this paper,we proposed a deep learning based image classification-restoration method to suppress the influence of atmospheric turbulence on computational ghost imaging.Specifically,the classification network based on a convolutional neural network classified images according to their blur degree.Then,the images of each class were restored by the restoration network based on a generative adversarial network.Furthermore,we established a compressive-sensing-based computational ghost imaging model including atmospheric turbulence.As a result,the blurred images caused by atmospheric turbulence of different intensities were obtained,and the blurred images were classified and restored by the deep learning method.The simulation results show that the proposed classification-restoration network can effectively improve the image quality of ghost imaging and significantly improve the structural similarity and peak signal-to-noise ratio of the restored images.Besides,this network can generalize different types of targets.