An infrared weak small target detection method based on tensor robust principal component analysis

A technology of principal component analysis and weak and small targets, which is applied in the field of infrared weak and small targets based on tensor robust principal component analysis, can solve the local optimal solution of nuclear norm and local structure weight, detect target distortion, and target detection accuracy low level problem

Active Publication Date: 2019-03-08
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0009] The object of the present invention is: the present invention provides a kind of infrared dim and small target detection method based on tensor robust principal component analysis, solves the nucl

Method used

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  • An infrared weak small target detection method based on tensor robust principal component analysis
  • An infrared weak small target detection method based on tensor robust principal component analysis
  • An infrared weak small target detection method based on tensor robust principal component analysis

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Embodiment 1

[0169] Such as Figure 1-16 As shown, a method for detecting small infrared targets based on tensor robust principal component analysis includes the following steps:

[0170] Step 1: Use the sliding window to traverse the original image to construct a third-order tensor;

[0171] Step 2: Calculate the second-order structure tensor of the original image, and construct the structure weight tensor according to the second-order structure tensor;

[0172] Step 3: Use the tensor robust principal component analysis to construct the objective function, input the third-order tensor and the structural weight tensor into the objective function, and use the alternating direction multiplier method to solve the objective function to obtain the background tensor and the target tensor;

[0173] Step 4: Reconstruct the background image and target image according to the background tensor and target tensor;

[0174] Step 5: Carry out adaptive threshold segmentation on the target i...

Embodiment 2

[0177] Based on embodiment 1, step 1 includes the following steps:

[0178] Step 1.1: Acquire the infrared image D∈R to be processed m×n , with a size of 240×320;

[0179] Step 1.2: Use a sliding window w with a size of 50×50 to traverse the original image D with a step size of 50, and use the matrix with a size of 50×50 in each sliding window w as a frontal slice;

[0180] Step 1.3: Repeat step 1.2 according to the number of window slides until the traversal is complete, and form all frontal slices into a new tensor

[0181] Such as figure 2 As shown in , it represents an infrared image with a complex background, in addition to weak and small targets, there are also high-brightness white false alarm sources; for example image 3 As shown, it represents the third-order tensor constructed from the original image after step 1

[0182] Step 2.1: Compute the structure tensor J of D ρ ∈ R 480×640 :

[0183]

[0184] Among them, K 2 Indicates...

Embodiment 3

[0196] Based on embodiment 1 or 2, step 3 includes the following steps:

[0197] Step 3.1: Input the third-rank tensor and structure weight tensor Combining tensor kernel norm and tensor l 1 Norm, build the objective function;

[0198] Step 3.2: and After inputting the objective function, use the alternate direction multiplier method to solve the objective function and solve the background tensor and the target tensor

[0199] Step 3.1 includes the following steps:

[0200] Step 3.1.1: Assume a third rank tensor from low-rank tensors and sparse tensors composed of separate and ε, construct the objective function as follows:

[0201]

[0202]

[0203] Among them, λ represents the balance coefficient, ||g|| * Indicates tensor kernel norm, ||g|| 1 represents the tensor l 1 norm,

[0204] Step 3.1.2: Order express The result of doing discrete Fourier transform (Discrete FourierTransform, DFT) along the third d...

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Abstract

The invention discloses an infrared weak small target detection method based on tensor robust principal component analysis, and relates to the field of infrared image processing and target detection.The method comprises the steps of 1 traversing an original image to construct a third-order tensor; 2 calculating a second-order structure tensor of the original image, and constructing a structure weight tensor; 3 using the tensor robust principal component analysis for constructing an objective function, inputting a third-order tensor and a structure weight tensor into the objective function, and using an ADMM for solving the objective function to obtain a background tensor and an objective tensor; 4 reconstructing a background image and a target image according to the background tensor andthe target tensor; 5 segmenting the target image and outputting a target detection result. According to the method, the problem that the target detection accuracy is low due to the fact that the nuclear norm and the local structure weight adopted in an existing method easily cause local optimal solution and detection target distortion is solved, and the effects of improving the target detection and background inhibition capability and enhancing the target shape keeping capability are achieved.

Description

technical field [0001] The invention relates to the field of infrared image processing and target detection, in particular to an infrared weak and small target detection method based on tensor robust principal component analysis. Background technique [0002] Infrared imaging technology has the characteristics of non-contact and strong ability to capture details, and is not affected by obstacles such as smoke and fog to achieve continuous long-distance target detection day and night; Infrared search and track IRST (Infrared search and track) system is used in military, It has been widely used in civil and other fields. As a basic function of the IRST system, infrared weak and small target detection technology is of great significance in infrared search, infrared early warning, and long-distance target detection. However, due to the lack of texture and structure information of the target in the infrared band, and the influence of long-distance, complex background, and various...

Claims

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Application Information

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IPC IPC(8): G06K9/32
CPCG06V10/255
Inventor 张兰丹彭真明刘雨菡宋立李美惠曹思颖彭凌冰黄苏琦何艳敏赵学功杨春平
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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