Infrared dim target detection based on total variation regularization and principal component pursuit

Wang, Xiaoyang; Peng, Zhenming; Kong, Dehui; Zhang, Ping; He, Yejun

doi:10.1016/j.imavis.2017.04.002

Cited by 115 publications

(69 citation statements)

References 26 publications

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“…We compare the proposed method with six baseline methods: Top-hat [7], Max-mean [5], Max-median [5], IPI [22], TV-PCP [23], and MLMC [16]. Table 3 presents the specific parameter setting for each method.…”

Section: Baseline Methods and Parameter Settingmentioning

confidence: 99%

“…e core idea of this kind of approach is to seek the best sparse and low-rank approximation of the given observation matrix, where the target is regarded as the sparse component and the background is lowrank component. RPCA framework has been widely applied to infrared small target detection [22][23][24][25][26] for its generalization and computation efficiency and can be extended to cope with large-scale problems. In realistic applications, sparse and lowrank recovery normally cannot be directly applied to small target detection because the decomposed background image matrix usually does not satisfy the low-rank characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…e small target detection task is converted into an optimization problem of recovering low-rank and sparse matrices, which can be solved efficiently using existing optimization solution techniques. Wang et al [23] introduced a total variation (TV) constraint on the background into the RPCA framework for better preserving the edges and corners of background region so as to improve the background estimation and achieve good detection results. Yao et al [25] tackled infrared small target detection using a coarse-to-fine two-phase method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Baseline Methods and Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Of which, detection algorithms based on background modeling mainly are composed of traditional detection algorithms based on time domain [2], space domain [3,4] or frequency domain filtering [5] and background modeling methods based on statistical characteristics [6,7], etc. Detection algorithms based on machine learning mainly include detection algorithms based on visual saliency [8,9], detection algorithms based on dictionary learning and sparse representation [10,11], detection algorithms based on low-rank background [12,13], and detection algorithms based on based on CNN (Convolutional Neural Network) [14][15][16][17], etc. When the target is in a low SNR scene, using merely the single-frame information to detect dim-small targets in the above singleframe filtering detection algorithm may result in a high false alarm rate in the detection results.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

Fan

2019

International Journal of Optics

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In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

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“…ability of infrared dim small targets. With the continuous development of infrared imaging detection system, algorithms for small target detection and recognition have been emerging in recent years [3][4][5][6][7][8][9][10][11]. However, because there are a large number of natural landscapes with high radiation in the imaging band of infrared images, such as cirrus, which is similar to the target in the satellite infrared image and has high gray level, it may cause false alarm of early warning system and interfere with small target detection, thus, it is difficult to detect small targets directly.…”

mentioning

confidence: 99%

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

et al. 2020

Self Cite

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In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

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Infrared dim target detection based on total variation regularization and principal component pursuit

Cited by 115 publications

References 26 publications

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

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