LD夜视成像探测中基于PSF的超分辨率重建技术研究

夜视目标识别在视频监控中起着非常重要的作用。从早期的红外热成像发展至今，激光夜视成像系统被广泛的应用。它不仅受到大气吸收和散射特性的影响，还与光学系统中的透镜及传感器的衍射极限有关。在既有成本下，通过图像复原和超分辨率重建，可以尽可能的提高夜视成像的成像质量。很多学者的研究结果表明，成像系统的点扩散函数作为先验知识，可以提高图像复原的质量。因此，本文在最大后验概率框架下，实现点扩散函数与凸集投影超分辨率重建相结合，并应用到半导体激光夜视成像探测系统中，客观的图像质量评价标准被用来决定迭代次数。目标识别的实验结果表明，用本文提出的重建方法可以有效提高半导体激光夜视成像的范围和成像质量。

Research on PSF-Based Super-Resolution Reconstruction in Night Vision LD Imaging Detection System

Night vision based target recognition plays a vital role in video surveillance, in which laser imaging detection is widely used. Since infrared thermal imaging until today, many developed countries have applied laser imaging detection. It is known that imagery detection is affected not only by absorption and scattering properties of atmosphere, but also the optical systems including lenses and sensors. In order to enhance the visual quality of the night vision detecting images to a best possible level, applications such as restoration and super-resolution recon-struction can be applied. The presented effort applies a spatial adaptive regularized MAP reconstruction method to a night vision laser diode（LD） imaging detection system. Blind, objective image quality metrics are adopted for de-termining the iteration time. Experimental results show adequate improvement in the detecting range as well as the detecting quality provided by the proposed approaches.