An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism

Chen, Yu‐Wen; Xin, Yunhong

doi:10.1109/lgrs.2016.2556218

Cited by 84 publications

(28 citation statements)

References 25 publications

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“…Acronyms Parameter Settings TopHat method [14] TopHat structure shape: square, size 3 3 × MaxMedian filter [9] MaxMedian support size: 5 5 × The filtering and saliency-based methods concentrate on how to pop out or enhance targets and suppress backgrounds as much as possible. In the following experiments, the comparative methods contain two classical filtering methods, namely TopHat [14] and MaxMedian [9], and three state-of-the-art saliency-based methods, namely Weighted Local Difference Measure (WLDM) [21], Multiscale Patch-based Contrast Measure (MPCM) [22], Local Saliency Map (LSM) [20]. We list the five experimental methods and their detailed parameter settings in Table 4.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by the soft-thresholding algorithm (STA) [53], we design a softening half -thresholding algorithm (SHTA), which is defined as: (20) and T = 3 √ 54 4 (λ) 2/3 . Accordingly, the matrix softening half-thresholding operator is defined as:…”

Section: Solution Of Rs 1/2 Nipi Modelmentioning

confidence: 99%

“…Chen et al [16] provided a clue to simply use local contrast measurement as enhancement factor to pop out small targets and suppress background. After that, there are a series of improved schemes proposed one after another, such as improved/novel local contrast method (ILCM/NLCM) [17,18], relative local contrast measure (RLCM) [19], local saliency map (LSM) [20], weighted local difference measure (WLDM) [21], multiscale patch-based contrast measure (MPCM) [22] and its improved versions [23,24]. Furthermore, the local entropy quantifying the complex degree of local gray distribution has been absorbed into the local contrast method to highlight small targets [25,26].…”

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: Methodsmentioning

confidence: 99%

Section: Solution Of Rs 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

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“…Another class of local prior based methods exploits the local contrast, which is computed by comparing a pixel or a region only with its neighbors. The seminal work of Laplacian of Gaussian (LoG) filter based method [14] has motivated a broad range of studies on the Human Visual System (HVS), and has led to a series of HVS based methods, e.g., Difference of Gaussians (DoG) [15], second-order directional derivative (SODD) filter [16], local contrast measure (LCM) [17], improved local arXiv:1703.09157v1 [cs.CV] 27 Mar 2017 contrast measure (ILCM) [18], multiscale patch-based contrast measure (MPCM) [19], multiscale gray difference weighted image entropy [20], improved difference of Gabors (IDoGb) [21], local saliency map (LSM) [22], weighted local difference measure (WLDM) [23], local difference measure (LDM) [24], etc.…”

Section: A Prior Work On Single-frame Infrared Small Target Detectionmentioning

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

313

169

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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 low signal-to-clutter ratio (SCR) and signal-to-noise ratio (SNR) make the infrared targets very faint. Therefore, robust infrared small and faint target detection technique remains a valuable research hotspot [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

Sun

Yang

et al. 2019

Remote Sensing

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The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches.

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An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism

Cited by 84 publications

References 25 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-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

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