scholarly journals Robust Principal Component Analysis-Based Infrared Small Target Detection

2019 ◽  
Vol 33 ◽  
pp. 9925-9926
Author(s):  
Qiwei Chen ◽  
Cheng Wu ◽  
Yiming Wang
Keyword(s):  
Iteration Process ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Small Target ◽  
Frame Sequence ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Infrared Small Target ◽  
Augmented Lagrange Multiplier Method

A method based on Robust Principle Component Analysis (RPCA) technique is proposed to detect small targets in infrared images. Using the low rank characteristic of background and the sparse characteristic of target, the observed image is regarded as the sum of a low-rank background matrix and a sparse outlier matrix, and then the decomposition is solved by the RPCA. The infrared small target is extracted from the single-frame image or multi-frame sequence. In order to get more efficient algorithm, the iteration process in the augmented Lagrange multiplier method is improved. The simulation results show that the method can detect out the small target precisely and efficiently.

Small Target Detection for Infrared Image Based on Optimal Infrared Patch-Image Model by Solving Modified Adaptive RPCA Problem

2020 ◽  
pp. 2150007
Author(s):  
Bin Xiong ◽  
Xinhan Huang ◽  
Min Wang ◽  
Gang Peng
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Low Rank ◽  
Small Target ◽  
Image Model ◽  
Threshold Method ◽  
Small Target Detection ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Patch Image

Small target detection in infrared (IR) images has been widely applied for both military and civilian purposes. In this study, because IR images contain sparse and low-rank features in most scenarios, we propose an optimal IR patch-image (OIPI) model-based detection method to detect small targets in heavily cluttered IR images. First, the OIPI model was generated based on a conventional IR image model using a novel optimal patch size and sliding step adaptive selection algorithm. Secondly, the sparse and low-rank features of IR images were extracted and fused to generate an adaptive weighted parameter. Thirdly, the adaptive inexact augmented Lagrange multiplier (AIALM) algorithm was applied in the OIPI model to solve the robust principal component analysis (RPCA) optimization problem. Finally, an adaptive threshold method is proposed to segment and calibrate targets. Experimental results indicate that the proposed algorithm is capable of detecting small targets more stably and accurately, compared with state-of-the-art methods.

scholarly journals Infrared Small Target Detection Based on Non-Overlapping Patch Model via l0-l1 Norm

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1426
Author(s):  
Jiaqi Yang ◽  
Yi Cui ◽  
Fei Song ◽  
Tao Lei
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Optimization Method ◽  
Low Rank ◽  
Computational Time ◽  
Background Suppression ◽  
Small Target ◽  
L1 Norm ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection technology has sufficient applications in many engineering fields, such as infrared early warning, infrared tracking, and infrared reconnaissance. Due to the tiny size of the infrared small target and the lack of shape and texture information, existing methods often leave residuals or miss the target. To address these issues, a novel method based on a non-overlapping patch (NOP) joint l0-l1 norm is proposed with the introduction of sparsity regularized principal component pursuit (SRPCP). The NOP model makes the patch lighter in the first place, reducing time consumption. The adoption of the l0 norm enhances the sparsity of the target, while the adoption of the l1 norm enhances the robustness of the algorithm under clutter. As a smart optimization method, SRPCP solves the NOP model fittingly and achieves stable separation of low-rank and sparse components, thereby improving detection capacity while suppressing the background efficiently. The proposed method ultimately yielded favorable detection results. Adequate experiment results demonstrate that the proposed method is competitive in terms of background suppression and true target detection with respect to state-of-the-art methods. In addition, our method also reduces the computational time.

scholarly journals Tensor Robust Principal Component Analysis via Non-Convex Low Rank Approximation

2019 ◽  
Vol 9 (7) ◽  
pp. 1411 ◽  
Author(s):  
Shuting Cai ◽  
Qilun Luo ◽  
Ming Yang ◽  
Wen Li ◽  
Mingqing Xiao
Keyword(s):  
Principal Component Analysis ◽  
Critical Role ◽  
Principal Component ◽  
Component Analysis ◽  
Current Approach ◽  
Low Rank ◽  
Tensor Rank ◽  
Robust Principal Component Analysis ◽  
Augmented Lagrange Multiplier ◽  
Convex Regularization

Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, we developed a new t-Gamma tensor quasi-norm as a non-convex regularization to approximate the low-rank component. Compared to various convex regularization, this new configuration not only can better capture the tensor rank but also provides a simplified approach. An optimization process is conducted via tensor singular decomposition and an efficient augmented Lagrange multiplier algorithm is established. Extensive experimental results demonstrate that our new approach outperforms current state-of-the-art algorithms in terms of accuracy and efficiency.

Sign in / Sign up

Export Citation Format

Share Document