Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Dai, Yawen; Wu, Yiquan

doi:10.1109/jstars.2017.2700023

Cited by 296 publications

(173 citation statements)

References 52 publications

(65 reference statements)

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“…The compared nonlocal correlation-based models include Stable Multi-subspace Learning (SMSL) [36], Infrared Patch-Image model (IPI) [32], Reweight Infrared Patch-Image model (ReWIPI) [33], Non-negative Infrared Patch-Image based on Partial Sum minimization of singular values (NIPPS) [42], Reweight Infrared Patch-Tensor model (RIPT) [34]. The objective functions and parameter settings for each model are listed in Table 2.…”

Section: Comparison To the State-of-the-art Methodsmentioning

confidence: 99%

“…It would lead to a dilemma where the weak targets are over-shrunk, resulting missing detection or the rare structures might be divided into target component, causing false alarm. Inspired by reweighted sparse enhancement scheme [38], some methods [33,34] have been proposed to get rid of this predicament by adopting different weight to penalize the different elements. However, although these methods can suppress the rare structures effectively, they ignore the intrinsic geometry of structural targets.…”

Section: Reweighted S 1/2 Nipi Modelmentioning

confidence: 99%

“…To solve these problems, Guo et al [33] suggested to employ a reweighted robust principal analysis model (ReWIPI). Dai et al [34] and Zhang et al [35] came up with a reweighted infrared patch-tensor (RIPT) model in which the local and nonlocal prior were integrated to adjust the constant sparse penalty parameter. Moreover, some methods used the multi-space property as structure measure to give a more exact background description, such as stable multi-space learning (SMSL) [36], low-rank and sparse representation model [37].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

Zhou

Dai

et al. 2019

Remote Sensing

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In uniform infrared scenes with single sparse high-contrast small targets, most existing small target detection algorithms perform well. However, when encountering multiple and/or structurally sparse targets in complex backgrounds, these methods potentially lead to high missing and false alarm rate. In this paper, a novel and robust infrared single-frame small target detection is proposed via an effective integration of Schatten 1/2 quasi-norm regularization and reweighted sparse enhancement (RS1/2NIPI). Initially, to achieve a tighter approximation to the original low-rank regularized assumption, a nonconvex low-rank regularizer termed as Schatten 1/2 quasi-norm (S1/2N) is utilized to replace the traditional convex-relaxed nuclear norm. Then, a reweighted l1 norm with adaptive penalty serving as sparse enhancement strategy is employed in our model for suppressing non-target residuals. Finally, the small target detection task is reformulated as a problem of nonconvex low-rank matrix recovery with sparse reweighting. The resulted model falls into the workable scope of inexact augment Lagrangian algorithm, in which the S1/2N minimization subproblem can be efficiently solved by the designed softening half-thresholding operator. Extensive experimental results on several real infrared scene datasets validate the superiority of the proposed method over the state-of-the-arts with respect to background interference suppression and target extraction.

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Section: Comparison To the State-of-the-art Methodsmentioning

confidence: 99%

Section: Reweighted S 1/2 Nipi Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

Zhou

Dai

et al. 2019

Remote Sensing

Self Cite

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“…Unfortunately, either generating artificially or learning desired dictionaries to adapt to most scenarios is not easy but complex, especially when more dictionaries are needed. To dig out more useful information from the nonlocal configuration in patch space, Dai et al [42] firstly generalized the IPI model to a novel infrared patch-tensor (IPT) model with the assumption that all the unfolding matrices are low rank, resulting in improved detection ability and reduction of computation time.…”

Section: Related Work On Single-frame-based Infrared Small Target Dementioning

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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“…To solve this problem, many methods were proposed in recent processing a scene with strong edges [38]. Although the following methods [39][40][41][42][43] obtain a remarkable progress to remove the edge residuals, they can hardly eliminate the strong local clutters of various shapes completely by employing a specific sophisticated norm to replace the nuclear norm.…”

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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Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Cited by 296 publications

References 52 publications

Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

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

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

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