Improved TLBO for Fusion of Infrared and Visible Images

Abstract

Image fusion is an image enhancement method in modern artificial intelligence theory, which can reduce the pressure in data storage and obtain better image information. Due to different imaging principles, information of the infrared image and visible images’ information is complementary and redundant. The infrared image can be fused with a visible image to obtain both the high-resolution texture details and the edge contour of the infrared image. In this paper, the fusion algorithm of forest sample image is studied at the feature level, which aims to accurately extract tree features through information fusion, ensure data stability and reliability, and improve the accuracy of target recognition. The main research contents of this paper are as follows: (1) teaching learning-based optimization (TLBO) algorithm was used to optimize the weighted coefficient in the fusion process, and the value range of random parameters in the model was adjusted to optimize the fusion effect. Compared with before optimization, image information increased by 2.05%, and spatial activity increased by 15.27%. (2) Experimental data show that the target recognition accuracy of feature-level fusion results was 93.6%, 13.9% higher than that of the original infrared sample image, and 18.8% higher than that of the original visible sample image. Pixel-level and feature-level fusion have their characteristics and application scopes. This method can improve the quality of the specified region in the image and is suitable for detecting intelligent information in forest regions.