Infrared Small Target Detection Based on Gradient Correlation Filtering and Contrast Measurement

Abstract

Infrared small target detection under complex backgrounds, especially in dense cloud and changeable clutter scenes, has always been a challenging research task. In order to improve the detection ability of small targets under complex backgrounds, an infrared small target detection method based on gradient correlation filtering and gradient contrast measurement (GCF-CM) is proposed in this paper. The infrared gradient vector field (IGVF) of the original image is first constructed through the facet model. Then, considering the unique gradient characteristics of small targets, a gradient correlation filtering (GCF) method is proposed to filter small targets and background clutters. Meanwhile, a gradient contrast measurement (GCM) method is designed to further enhance the intensity of the small target. Finally, after fusing the two response maps, an adaptive threshold is adopted to extract small targets. Experimental results demonstrate that the proposed method can improve the intensity of the small target and suppress clutter sufficiently. In comparison with other excellent methods, the proposed method exhibits a robust detection performance.