Dim Target Detection in Airborne Infrared Images Based on Visual Feature Fusion

In this paper, a dim target detection method in airborne infrared images based on visual feature fusion is proposed. The proposed method aims at improving the high false alarm rate or low detection rate achieved by existing methods in complex cloud and strong clutter interference environments. Initially, the original image is sharpened using Laplace algorithm to extract the contour edge, which is added to the original image. The purpose is to enhance the pixel intensity of real and suspected targets. Subsequently, based on the gradient characteristics of the targets, the local multidirectional gradient method is used to suppress the complex background and strong clutter in processed images. Next, based on the gray difference characteristics of the images, the local gray difference method is employed to properly enhance the target. Finally, the images acquired by visual feature information arc fused to highlight the saliency of the targets, and the adaptive threshold is used to achieve accurate target detection. The experiment results verify that compared with other methods, the proposed method significantly improves the signal-to-clutter ratio, background suppression factor, and detection rate. It also achieves a lower false alarm rate.