Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Abstract

Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

“…For complex backgrounds (Figs. [11][12][13][14][15][16][17], most of the methods can detect the targets, but some target images contain abundant of clutters, resulting in higher false alarm rates. The results obtained by the two classical methods, IPIAPG, MSRLCM, MSAAGD, and PSTNN show that the noise is widely distributed.…”

“…In addition, Li et al [22] proposed a dual-window local contrast method for preprocessing and then used a multiscale window IPI to extract features. Certainly, there are also some nonIPI-based approaches have been proposed to infrared dim small target detection; see [2], [10], [16], [23], [42] for example. Overall, the dim small target detection method based on the framework of IPI has obtained abundant achievements.…”

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

“…The radiation intensity of the target is independent of the surrounding background, and generally higher than that of the local background. The infrared radiation intensity of the local background area has a strong spatial correlation, so that the neighborhood information can be used to reconstruct the background covered by the target [8].…”

“…Existing background estimation method cannot accurately reconstruct the background image. Whether it is a multi-window or a protection window mechanism, it is affected by the target pixels on background estimation [8] [17] [18]. Therefore, the aim of this paper to use image inpainting technology to accurately reconstruct the background, and propose a new infrared small target detection algorithm to solve these shortcomings and weaknesses.…”

Multidirectional Ring Top-Hat Transformation for Infrared Small Target Detection

“…NFRARED small target detection is one of the key techniques in many fields, including maritime rescue and surveillance [1], precision guidance [2], remote sensing [3], and infrared searching and tracking systems [4]. Due to a long imaging distance, small targets can often be spot-like, lacking effective shape and texture information.…”