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Night vision image fusion for target detection with improved 2D maximum entropy segmentation

Infrared and LLL image are used for night vision target detection. In allusion to the characteristics of night vision imaging and lack of traditional detection algorithm for segmentation and extraction of targets, we propose a method of infrared and LLL image fusion for target detection with improved 2D maximum entropy segmentation. Firstly, two-dimensional histogram was improved by gray level and maximum gray level in weighted area, weights were selected to calculate the maximum entropy for infrared and LLL image segmentation by using the histogram. Compared with the traditional maximum entropy segmentation, the algorithm had significant effect in target detection, and the functions of background suppression and target extraction. And then, the validity of multi-dimensional characteristics AND operation on the infrared and LLL image feature level fusion for target detection is verified. Experimental results show that detection algorithm has a relatively good effect and application in target detection and multiple targets detection in complex background.