Design And Research Of Night Vision Helmet Based On Low Light Level And Infrared Fusion Imaging

Because the detection band of the low-light or infrared detection system is single and it is difficult to make full use of scene information,it is necessary to research lowlight and infrared fusion imaging to combine the image characteristics of different bands and improve the utilization rate of image information.According to the requirements of miniaturization and low power consumption in night vision,security,and other fields,this dissertation develops the design and research of a night vision helmet fusion system based on low-light and infrared fusion.Firstly,aiming at the requirements of small volume and low power consumption of night vision helmet,a dual-channel parallel optical axis optical system was designed to ensure the small volume and the imaging quality of night vision helmet.The image fusion circuit is designed with the custom infrared movement and SC2210 low light detector as the front-end detection module,and the RV1126 chip as the core of the image fusion circuit,with the imaging system necessary front-end detection,power management,image transmission,and other basic function modules.At the same time,it has extended functions such as display,exposure adjustment,and so on.Secondly,by analyzing the imaging characteristics of the infrared image,aiming at the low contrast of the infrared image,the adaptive piecewise nonlinear transformation is used to enhance the infrared image.On this basis,the contour pattern algorithm of infrared images is studied,the edge of infrared images is extracted by the gray morphology anti-noise operator and the algorithm is integrated into the FPGA of the infrared movement.The experimental results show that the edge extracted by the proposed contour pattern algorithm is better than the traditional contour extraction algorithm.Finally,to improve the significance of the target in the image,this dissertation proposes an image fusion algorithm based on the weight reconstruction of significance detection based on the traditional image fusion algorithm.The core idea of the algorithm is to separate the basic layer and the detail layer of the image through adaptive guided filtering to detect the significance of the basic layer of the image and make full use of the significance of the target in the infrared image and the low-light level image.The fusion image can retain significant information related to the two images experimental results show that the objective evaluation indexes such as mutual information of the fused image are better than those of the image fusion algorithm.Transplant the image fusion algorithm in this dissertation to the night vision helmet.The night vision helmet was debugged and an imaging experiment was carried out.The results showed that the functions of the night vision helmet were normal,the overall mass was 196 g,and the volume was 80×70×40mm,in the fusion mode,the total power consumption of the system is 2.28 W,and the frame rate is 30 Hz.