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Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model
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DOI：10.1109/ACCESS.2022.3164184.png)
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摘要
En 中文
In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.
Keyword:
Object detection
Signal to noise ratio
Tensors
Target tracking
Filtering
Filtering algorithms
Pipelines
Infrared dim and small target detection
principal component decomposition model
gradient difference regularization principal component decomposition model
overlapping directional multiplier method
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Infrared Small Target Detection Through Multiple Feature Analysis Based on Visual Saliency.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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