scholarly journals Infrared Patch-Tensor Model With Weighted Tensor Nuclear Norm for Small Target Detection in a Single Frame

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76140-76152 ◽  
Author(s):  
Yang Sun ◽  
Jungang Yang ◽  
Yunli Long ◽  
Zheran Shang ◽  
Wei An
Keyword(s):  
Target Detection ◽  
Nuclear Norm ◽  
Tensor Model ◽  
Small Target ◽  
Small Target Detection ◽  
Single Frame ◽  
Tensor Nuclear Norm

scholarly journals Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019 ◽  
Vol 11 (4) ◽  
pp. 382 ◽  
Author(s):  
Landan Zhang ◽  
Zhenming Peng
Keyword(s):  
Real Time ◽  
Target Detection ◽  
State Of The Art ◽  
Nuclear Norm ◽  
Tensor Model ◽  
Small Target ◽  
Small Target Detection ◽  
Partial Sum ◽  
Tensor Nuclear Norm ◽  
Infrared Small Target

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Single frame infrared small target detection based on local gradient and directional curvature

2021 ◽  
Author(s):  
Minjie Wan ◽  
Yunkai Xu ◽  
Qinyan Huang ◽  
Weixian Qian ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Target Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Single Frame ◽  
Local Gradient ◽  
Infrared Small Target
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