IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

Abstract

Small target detection is a crucial and challenging task in infrared search and track system. Background estimation-based methods is an effective and important approach for infrared small target detection. Affected by the target pixels, existing background estimation methods may reconstruct an inaccurate background. Based on image inpainting technique, we propose a novel two-stage approach to predict more accurate backgrounds. At the first stage, the inner and outer window-based image inpainting (IOWII) is used to obtain a rough background estimation. Then a mask of candidate target region is automatically obtained by calculating and evaluating the difference between raw image and rough background. In the second stage, the final accurate background is predicted by mask-based image inpainting (MII). It recovers the removed candidate target area using the information of surrounding background pixels, avoiding target pixels to participate in the calculation of background reconstruction. Finally, the target saliency map is obtained by subtracting the final estimated background from the original image, and a simple adaptive threshold is used to segment the target. Experimental results on real infrared images and sequences demonstrate that the proposed method outperforms other state-of-the-arts. It is simple and effective, with strong robustness and good real-time performance.