Infrared small-dim target detection based on Markov random field guided noise modeling

Gao, Chenqiang; Wang, Lan; Xiao, Yongxing; Zhao, Qian; Meng, Deyu

doi:10.1016/j.patcog.2017.11.016

Cited by 151 publications

(68 citation statements)

References 23 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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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“…Many approaches have been reported for addressing these issues, which roughly include two classes of mainstream detection methods: sequential detection [5,6] and single-frame detection [7,8]. Traditional sequential detection methods are driven by prior information such as target trajectory, velocity and shape, and essentially utilize the adjacent inter-frame knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…Representative multiple targets images from the datasets and the separated target images obtained by six low-rank recovery-based methods (5)(6)(7)(8). are four representative multiple targets images from the tested datasets.…”

mentioning

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

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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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“…To this end, a state-of-the-art method [32] digs out valuable information in time domain and uses a mixture of Gaussian (MoG) noise models [33] to model the target component and noise component together. The MoG model characterizes each pixel in the image and updates the mixed Gaussian model after the new image is acquired.…”

Section: Introductionmentioning

confidence: 99%

“…However, the noise distribution in different frames is modeled as i.i.d. MoG distributions substantially in [32], which is not suitable for complex noisy scenarios. In addition, the MRF model does not provide a robust noise estimate in complex scenarios, since its performance is based on the assumption that the noise component does not arise in the neighborhood region of the targets.…”

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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Infrared small-dim target detection based on Markov random field guided noise modeling

Cited by 151 publications

References 23 publications

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

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

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

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