A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window

Han, Jinhui; Moradi, Saed; Faramarzi, Iman; Liu, Chengyin; Zhang, Honghui; Zhao, Qian

doi:10.1109/lgrs.2019.2954578

Cited by 189 publications

(64 citation statements)

References 26 publications

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“…To verify the advantage of the proposed algorithm, eight state-of-the-art algorithms are chosen for comparison, namely DoG [18], ILCM [8], NLCM [28], MPCM [26], RLCM [27], WLDM [33], MDTDLMS [32], and the recent, multiscale tri-layer local contrast measure (TLLCM) [44]: The cell size is 9 × 9. Three scales are used, and (K1, K2) is set to (2, 4), (5,9), and (9, 16), respectively.…”

Section: Comparisons With Other Algorithmsmentioning

confidence: 99%

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

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Section: Comparisons With Other Algorithmsmentioning

confidence: 99%

“…For TLLCM, the computational complexity is analyzed in the original paper [44] and is O(SL S 2 log(L S 2 )XY).…”

Section: ) Comparison Of Computational Complexity and Time Consumptionmentioning

confidence: 99%

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

Han

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The detection results of the proposed method when a pixel is a true target [39], background edge, high-brightness background, and pixel-sized noises with high brightness (PNHB) are presented in Fig. 5.…”

Section: B Multidirectional Ring Top-hat Transformationmentioning

confidence: 99%

Multidirectional Ring Top-Hat Transformation for Infrared Small Target Detection

Wang

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fast and robust detection of a small target in an infrared image is one of the key techniques in infrared search and track systems. The Top-Hat transformation is an important technology in the field of infrared target detection. Many algorithms have been proposed to improve the Top-Hat transformation by modifying different structural elements. However, these methods can cause a high false alarm rate under conditions of small dim targets, low signal-to-noise, and complex background. To overcome this limitation, considering both detection performance and speed, an effective and robust detection model using multidirectional structural elements is proposed in this article. First, the non-concentric multidirectional ring structural elements are designed. Then, using the morphological transformation with designed structural elements, the background is suppressed, and a small target is enhanced in different directions. Finally, the results of the morphological transformations are fused to obtain the final result, further highlighting a small target. The experimental results demonstrate the robust performance of the proposed method for various backgrounds and targets. In addition, the proposed method can achieve a detection speed of up to 10.4 ms per frame under the image resolution of 240 × 320 pixels. Therefore, the proposed method is suitable for real-time applications.

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“…The latter tends to be inefficient and is often unable to perform real-time end-to-end detection [3]. Traditional methods for detecting infrared small targets based on single-frame detection include filter-based methods [4,5], local contrast-based methods [6][7][8][9], and low-rankbased methods [10][11][12][13]. However, these traditional methods based on handcraft fixed sliding windows, step sizes, and fixed hyperparameters are incapable of detecting targets accurately when the characteristics of the real scene (e.g., target size, shape, and background clutter) change significantly from those expected.…”

Section: Introductionmentioning

confidence: 99%

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

et al. 2021

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Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.

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A Local Contrast Method for Infrared Small-Target Detection Utilizing a Tri-Layer Window

Cited by 189 publications

References 26 publications

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

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

Multidirectional Ring Top-Hat Transformation for Infrared Small Target Detection

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

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