噪声环境下基于单像素成像系统和深度学习的目标识别方法

Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

单像素成像系统由于其独特的成像方式受到广泛关注,但其在噪声环境中的目标识别方法并未得到深入研究。针对该问题,文中分别采用桶探测器获取的信号值和重构出的二维图像作为训练样本进行深度学习,并以此识别噪声环境中的目标。通过对比两者识别结果,发现在采样率较低时,前者即使在较强噪声环境中也可以获得较高的识别率;而后者的识别率虽然一直比较稳定,但其预处理时间较高,因此前者更适用于快速成像中的目标识别。此外,对于仅利用桶探测器信号进行训练的方法,文中还研究了目标稀疏度对其识别精度的影响,发现当外界噪声和采样率一定时,稀疏度越高的目标,其识别精度也越高。文中为噪声环境中单像素成像的目标识别方法提供了选择依据。

Single-pixel imaging system attracts a lot of attentions because of its special imaging method,but its target recognition method in noisy environment has not been studied deeply.Aiming at this problem,the signal sequences obtained by the bucket detector and the corresponding formed two-dimensional images were used as the training samples for deep learning to identify targets in noisy environments.By comparing the recognition results of these two methods,it was found that when the sampling rate was low,the former one could obtain a higher recognition rate even in a strong noise environment;while for the latter one,although the recognition rate was relatively stable,its preprocessing time was high,so the former one was more suitable for target recognition in high-speed imaging.In addition,for the method using only the bucket detector signal as the training samples,the effect of target sparsity on its recognition accuracy was also analyzed.It was found that when the external noise and sampling rate were fixed,the higher the sparsity of the target,the higher the recognition accuracy was.This paper can be used as the reference for the selection of single pixel system recognition methods in noisy environments.