Tiny and Dim Infrared Target Detection Based on Weighted Local Contrast

Liu, Jie; He, Ziqing; Chen, Zuolong; Shao, Lei

doi:10.1109/lgrs.2018.2856762

Cited by 64 publications

(26 citation statements)

References 16 publications

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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%

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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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Section: Methodsmentioning

confidence: 99%

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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“…The grayscale-based measures that are used in most filtering methods are merely focusing on how to extract local prior such as local contrast [16,56,57], local entropy [58,59], and local difference [32,33,60]; nevertheless, this type of insufficient information is not enough to differentiate target and background. Conversely, optimizing methods with nonlocal property involved are more robust to complex scenes, but still suffer from background residuals in target components mainly because of the salient edges.…”

Section: Local Prior Analysismentioning

confidence: 99%

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019

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Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

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“…Based on the required number of image frames, the small infrared target detection algorithms can be divided into singleframe small infrared target detection algorithms and sequentialframes small infrared target detection algorithms. The sequential-frames-based algorithms usually rely on the assumption that the information on target and background is consistent between frames, and they require prior knowledge * Corresponding author about target shape and speed [5]. However, this prior knowledge is difficult to obtain in practical applications [6], so the single-frame small infrared target detection algorithms have been widely used.…”

Section: Introductionmentioning

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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Tiny and Dim Infrared Target Detection Based on Weighted Local Contrast

Cited by 64 publications

References 16 publications

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

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

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Multidirectional Ring Top-Hat Transformation for Infrared Small Target Detection

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