High-Boost-Based Multiscale Local Contrast Measure for Infrared Small Target Detection

Shi, Yafei; Wei, Yantao; Yao, Hongxun; Pan, Donghui; Xiao, Guangrun

doi:10.1109/lgrs.2017.2772030

Cited by 131 publications

(69 citation statements)

References 22 publications

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“…We compare the proposed method with six baseline methods: Top-hat [7], Max-mean [5], Max-median [5], IPI [22], TV-PCP [23], and MLMC [16]. Table 3 presents the specific parameter setting for each method.…”

Section: Baseline Methods and Parameter Settingmentioning

confidence: 99%

“…ese methods try to improve the ability to suppress the clutters and noise as well as enhance the target. Shi et al [16] proposed an enhanced high-boost-based multiscale local contrast measure (HB-MLCM) method to enhance the true target and suppress background clutters, and the experimental results verified the effectiveness of this method.…”

Section: Introductionmentioning

confidence: 93%

“…A large number of methods have been developed to address the issues of small target detection. ese methods can be roughly classified into two categories: single-frame detection [5][6][7][8][9][10][11][12][13][14][15][16] and sequential multiframe detection [17][18][19]. Recently, Gao et al [17] employed the mixture of the Gaussians model [20] with the Markov random field to model the complex noise of which the target is assumed as a component.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

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Infrared small target detection plays an important role in infrared search and tracking systems applications. It is difficult to perform target detection when only a single image with complex background clutters and noise is available, where the key is to suppress the complex background clutters and noise while enhancing the small target. In this paper, we propose a novel model for separating the background from the small target based on nonlocal self-similarity for infrared patch-image. A total variation-based regularization term for the small target image is incorporated into the model to suppress the residual background clutters and noise while enhancing the smoothness of the solution. Furthermore, a reweighted sparse constraint is imposed for the small target image to remove the nontarget points while better highlighting the small target. For higher computational efficiency, an adapted version of the alternating direction method of multipliers is employed to solve the resulting minimization problem. Comparative experiments with synthetic and real data demonstrate that the proposed method is superior in detection performance to the state-of-the-art methods in terms of both objective measure and visual quality.

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Section: Baseline Methods and Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

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“…Please note that most of these baseline methods are proposed in the last two years, and they can represent the highest level of infrared small target detection in the current period. The employed baseline methods are Min-Local-LoG method [60], the LS-SVM-based method [25], the multiscale patch based contrast measure (MPCM) [46], the high-boost-based multiscale local contrast measure (HB-MLCM) [47], the multiscale weighted local contrast measure (MWLCM) [48], the derivative entropy based contrast measure (DECM) [49], and the multiscale relative local contrast measure (RLCM) [50].…”

Section: Methodsmentioning

confidence: 99%

“…In 2013, Chen et al created a feature called the local contrast measure (LCM) to define the local contrast between the targets and background in infrared images [44]. Since then, plenty of other definitions of the local contrast, such as the improved difference of Gabor filter [45], the multiscale patch-based contrast measure (MPCM) [46], the high-boost-based multiscale local contrast measure (HBMLCM) [47], the multiscale weighted local contrast measure (MWLCM) [48], the derivative entropy-based contrast measure (DECM) [49], the relative local contrast measure (RLCM) [50], the Gaussian scale-space enhanced local contrast measure (GSS-ELCM) [51], the homogeneity-weighted local contrast measure (HWLCM) [52], and so on, were proposed. The above models compute the local feature value at each position by sliding a rectangle window, and they are usually concise and thus have a fast running speed.…”

Section: Introductionmentioning

confidence: 99%

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Huang

Liu

et al. 2019

Remote Sensing

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Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.Remote Sens. 2020, 12, 47 2 of 22 decades, and they can be generally grouped into two categories: track-before-detect (TBD)-based methods and detect-before-track (DBT)-based methods.The TBD-based methods, such as pipeline filtering [11], hypothesis testing [12], 3-D matched filtering [13], temporal profile filtering [14], dynamic programming [15] and so on, try to use the grayscale consistency and the trajectory continuity of the targets in consecutive frames so as to discriminate the small targets from noise [16][17][18][19]. These methods are based on two assumptions; one is that the motion model of the target is known, the other is that the background motion is slow. In ideal conditions, both assumptions are satisfied; the energy of the targets is accumulated in adjacent frames, and the difference between targets and noise is increased. As a result, the TBD-based methods perform well in low signal-to-clutter conditions. However, in practical applications, we can hardly acquire the precise motion model of the targets, and the backgrounds can move fast when the infrared detector is in a moving platform. Therefore, both of the two assumptions could fail in real situations, and the performance of the TBD-based methods could degrade significantly [20]. Meanwhile, high time and storage requirements also make these TBD approaches unsuited to large-scale engineering projects [21].Compared with TBD-based methods, DBT-bas...

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A Saliency-Transformer Combined Knowledge Distillation Guided Network for Infrared Small Target Detection

Zhang

Feng

et al. 2023

Lecture Notes in Electrical Engineering

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High-Boost-Based Multiscale Local Contrast Measure for Infrared Small Target Detection

Cited by 131 publications

References 22 publications

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

A Saliency-Transformer Combined Knowledge Distillation Guided Network for Infrared Small Target Detection

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