Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values

Dai, Yawen; Wu, Yiquan; Shen, Yu; Guo, Jun

doi:10.1016/j.infrared.2017.01.009

Cited by 149 publications

(90 citation statements)

References 30 publications

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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%

“…Some popular nonconvex regularizers include log-sum penalty [38], truncated nuclear norm [39], partial sum minimization of singular values [40] and Schatten p quasi-norm [41], and so on. Especially, Dai et al [42] used the partial sum minimization of singular values replacing the nuclear nom minimization to improve the small target detection rate. However, for this method, it is difficult to estimate a suitable rank to achieve exact detection in real situations.…”

Section: Motivationmentioning

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: Motivationmentioning

confidence: 99%

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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“…We call this phenomenon the rare structure effect. In contrast, the nonlocal prior methods [26], [29], [30] suffer from the salient edge residuals. Its intrinsic reason is because the strong edge is also a sparse component as the same as the target due to lack of sufficient similar samples.…”

Section: B Motivationmentioning

confidence: 99%

“…Baselines and Parameter settings. The proposed algorithm is compared with ten state-of-the-art solutions, including three filtering based methods (Max-Median [10], Top-Hat [48], TDLMS [9]), three HVS based methods (PFT [49], MPCM [19], WLDM [23]), and four recently developed lowrank methods (IPI [26], PRPCA [50], WIPI [29], NIPPS [30]). Tab.…”

Section: A Experimental Setupmentioning

confidence: 99%

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

Dai

2017

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

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296

173

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Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise localstructure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.Index Terms-infrared small target detection, infrared patchtensor model, reweighted higher-order robust principal component analysis, non-local self-correlation prior, local structure prior. Y. Dai is with the College

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“…The prior information is the key to the success of single-frame-based methods, also in many other fields [9][10][11]. Up to now, the consistency of backgrounds [12][13][14][15], the saliency of targets [16][17][18][19], the sparsity of targets and the low rank of backgrounds [20][21][22][23][24] are the most used assumptions to detect infrared small targets in single image from different perspectives. The former two are local priors, whereas the latter two are nonlocal priors which are usually exploited simultaneously.…”

Section: Introductionmentioning

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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Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values

Cited by 149 publications

References 30 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

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

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

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