Fast and Robust Infrared Small Target Detection Using Weighted Local Difference Variance Measure

Abstract

Infrared (IR) small-target-detection performance restricts the development of infrared search and track (IRST) systems. Existing detection methods easily lead to missed detection and false alarms under complex backgrounds and interference, and only focus on the target position while ignoring the target shape features, which cannot further identify the category of IR targets. To address these issues and guarantee a certain runtime, a weighted local difference variance measure (WLDVM) algorithm is proposed. First, Gaussian filtering is used to preprocess the image by using the idea of a matched filter to purposefully enhance the target and suppress noise. Then, the target area is divided into a new tri-layer filtering window according to the distribution characteristics of the target area, and a window intensity level (WIL) is proposed to represent the complexity level of each layer of windows. Secondly, a local difference variance measure (LDVM) is proposed, which can eliminate the high-brightness background through the difference-form, and further use the local variance to make the target area appear brighter. The background estimation is then adopted to calculate the weighting function to determine the shape of the real small target. Finally, a simple adaptive threshold is used after obtaining the WLDVM saliency map (SM) to capture the true target. Experiments on nine groups of IR small-target datasets with complex backgrounds illustrate that the proposed method can effectively solve the above problems, and its detection performance is better than seven classic and widely used methods.