Multiscale Local Gray Dynamic Range Method for Infrared Small-Target Detection

Abstract

The infrared small-target-detection algorithm has theoretical significance and military value. Meanwhile, it is also a challenging task, especially to enhance greatly the true targets from the intricate background clutters at a low signal-to-noise ratio. In this letter, a multiscale local gray dynamic range (MLGDR) method is presented based on the assumption that the target and the background have different gray dynamic ranges in the local areas. Consequently, the final MLGDR map is innovatively achieved by the two proposed properties, including the local multiscale differences in the gray distribution changes and in the gray values. The results of the experiments indicate that the proposed method is capable of enhancing the target and suppressing the background clutter simultaneously. In particular, compared with the baseline methods, our method achieved a high signal-to-noise ratio, a high detection rate, and a low false-alarm rate under various scenes.