Infrared Small Target Detection Based on Weighted Three-Layer Window Local Contrast

Abstract

The performance of small target detection restricts the development of the infrared search and track (IRST) system. Against the complicated background clutter of the infrared (IR) image, the small targets are difficult to separate from a noisy background. Aiming at solving the problem of residual background clutter in the local contrast method, a weighted three-layer window local contrast method (WTLLCM) is proposed in this letter. First, the images are filtered by a layered gradient kernel to enhance the contrast between targets and background. Then, a three-layer window is utilized to calculate the local contrast of the filtered images. Next, it is worth mentioning that a simple target aggregation strategy is considered to preserve the integrity of the target. Especially, a new region intensity level (NRIL) algorithm is proposed to weigh the local contrast map to further suppress the background. Finally, the targets are detected by adaptive threshold segmentation. Compared with state-of-the-art small targets detection baseline algorithms based on local contrast, extensive experimental results demonstrate the superiority of the proposed method, especially in complex backgrounds. And instead of utilizing multi-scale windows, multi-scale targets detection is accomplished by using a single-scale window to reduce the calculation of the method.