Infrared Small Target Detection Using Homogeneity-Weighted Local Contrast Measure

Abstract

Detecting small targets in infrared (IR) image sequences is an important task in IR guidance systems. The clutter of complex backgrounds often submerges small targets, making detection difficult. Achieving high detection and low false alarm rates with complex backgrounds is a primary problem. We propose an IR small target detection method using our new homogeneity-weighted local contrast measure (HWLCM). Inspired by the ability of the human visual system (HVS) to determine saliency characteristics, we implement our method to use the local contrast features of the central and surrounding regions and the weighted homogeneity characteristics of the surrounding regions to enhance the target while suppressing the complex background. Our method divides each image into blocks with a sliding window for which the HWLCM is calculated. The HWLCM enhances the actual target and suppresses interference simultaneously. We apply an adaptive threshold to target region extraction to further refine the results. Our experimental results show that our proposed method is more effective than six comparable methods, especially in terms of the signal-to-clutter gain (SCRG) and background suppression factor (BSF) indicators.