An Infrared Small Target Detecting Algorithm Based on Human Visual System

Han, Jinhui; Ma, Yong; Huang, Jun; Mei, Xiaoguang; Ma, Jiayi

doi:10.1109/lgrs.2016.2519144

Cited by 100 publications

(61 citation statements)

References 19 publications

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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%

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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

295

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

confidence: 99%

“…It is precisely the shrinking threshold of Eq. (21), which influences the low-rank property of the background patch-tensor. With a smaller µ, more details are preserved in the background patch-tensor.…”

Section: ) Penalty Factor µmentioning

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

295

169

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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%

“…However, these methods will lead to high false alarm rates and poor detection performance when the signal-to-clutter ratio is low. To better suppress the background clutters and noise while enhancing the small target, some methods [8,9] based on local contrast enhancement have been proposed. Chen et al [8] proposed a local contrast detection method by defining a new contrast measure inspired by the biological visual mechanism.…”

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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“…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%

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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An Infrared Small Target Detecting Algorithm Based on Human Visual System

Cited by 100 publications

References 19 publications

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

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

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

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

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