An Infrared Image Enhancement Algorithm Based on Guided Filter and Gradient Fusion

In the process of infrared image processing, effectively suppressing noise, enhancing image contrast and highlighting detail features are of vital importance. This paper proposes an infrared image enhancement algorithm based on guided filtering, adaptive weighted histogram equalization and image fusion. It takes the hierarchical processing of guided filtering images as the foundation and combines an improved histogram enhancement method to achieve detail enhancement of infrared images. The algorithm first uses guided filtering to decompose the input infrared image into a basic layer and a detail layer, where the basic layer retains the low-frequency information of the image, while the detail layer contains the high-frequency edge and detail information. For the basic layer, the CLAHE algorithm is used to enhance the contrast of the image. For the detail layer, the Sobel operator is utilized for sharpening to enhance the edges and fine details of the image, further highlighting the detail information. Finally, the enhanced basic layer and detail layer are fused with adaptive adjustment of weights based on the gradient information of the original image to generate the final enhanced image. Through the analysis of experimental results in different infrared scenes, this algorithm achieves the effect of suppressing noise and enhancing the contrast of infrared images.