A Novel Size-Aware Local Contrast Measure for Tiny Infrared Target Detection

Abstract

Detecting tiny infrared (IR) targets in diverse complex backgrounds faces many challenges, e.g., extremely few features of the tiny targets, cluttered backgrounds, and interferences from surrounding similar objects. In this letter, we propose a novel size-aware local contrast measure (SALCM) method to detect tiny IR targets. First, to tackle the problem of extremely few features, various local features are extracted through monogenic signal decomposition, which can effectively enrich the potential features of the tiny targets. Second, the Canny detector is used to precisely delineate the contours of multiple candidate targets in the fused image to estimate the exact shapes and sizes of candidate targets. This ensures that the proposed method adapts to both tiny targets and small targets (with relatively larger sizes). Finally, local contrast enhancement is used to highlight the target regions while suppressing the background clutters and interferences from surrounding similar objects, leading to accurate detection. The experimental results on six real IR target datasets demonstrate the superiority of the proposed method in terms of target enhancement, background suppression, and detection accuracy, for detecting IR targets of various sizes.