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SPCP infrared weak and small target detection method based on 3DATV constraints

A technology of weak and small targets and detection methods, applied in image data processing, instruments, calculations, etc., can solve problems such as poor adaptability, weak edge suppression, and low noise robustness

Active Publication Date: 2020-04-10
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Description
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  • Application Information

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Problems solved by technology

[0021] The purpose of the present invention is: the present invention provides a SPCP infrared weak and small target detection method based on 3DATV constraints, which overcomes the shortcomings of existing methods such as low noise robustness, weak edge suppression, and poor adaptability to complex scenes, etc., and enhances target detection. Shape retention improves the accuracy of target detection

Method used

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  • SPCP infrared weak and small target detection method based on 3DATV constraints
  • SPCP infrared weak and small target detection method based on 3DATV constraints
  • SPCP infrared weak and small target detection method based on 3DATV constraints

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Embodiment 1

[0126] Such as Figure 1-11 As shown, the SPCP infrared faint target detection method based on 3DATV constraints includes the following steps:

[0127] Step 1: Construct the third-order tensor of the original image D

[0128] Step 2: Extract the prior information of the original image D and construct the prior weight tensor

[0129] Step 3: Use Tensor Kernel Function||·|| * and tensor l 1 Norm, combined with three-dimensional anisotropic total variation (3DATV) constraints, constructs an objective function, transforms the original target detection problem into a stable principal component tracking (Stable Principle Component Pursuit, SPCP) problem, and uses ADMM Solve the objective function to obtain the background tensor and the target tensor

[0130] Step 4: According to the background tensor and the target tensor Reconstruct the background image B and the target image T;

[0131] Step 5: Carry out adaptive threshold segmentation on the target image T to det...

Embodiment 2

[0135] Based on Embodiment 1, the steps of this application are refined, and the technical means adopted to solve technical problems are recorded in detail: using tensor kernel function||·|| * and tensor l 1 Norm, combined with three-dimensional anisotropic total variation (3D Anisotropic TotalVariation, 3DATV) constraints, construct the objective function, transform the original target detection problem into a stable principal component tracking (Stable Principle Component Pursuit, SPCP) problem, and use ADMM Solve the objective function to obtain the background tensor and the target tensor.

[0136] Step 1 includes the following steps:

[0137] Step 1.1: Acquire the infrared image to be processed (raw image) The size is 140×220;

[0138] Step 1.2: Use a sliding window w with a size of 40×40 to traverse the original image D with a step size of 40, and use the matrix with a size of 40×40 in each sliding window w as a frontal slice;

[0139] Step 1.3: Repeat step 1.2 accor...

Embodiment 3

[0203] Based on embodiment 1, this embodiment refines step 2, extracts the prior information of the original image, constructs the prior information weight tensor, and utilizes the prior information related to the background and the target to ensure that the target is not distorted, which speeds up the algorithm. The convergence speed also improves the robustness of the algorithm.

[0204] Step 2 includes the following steps:

[0205] Step 2.1: Define the structure tensor of the original image D J ρ It is defined as follows:

[0206]

[0207] Among them, K ρ Indicates the Gaussian kernel function with variance 2, * indicates the convolution operation, D σ Indicates that the Gaussian smoothing filter with a variance of 9 is performed on the original image, represents the Kronecker product, Indicates the gradient, means D σ the gradient along the x direction, means D σ Gradient along the y direction, J 11 replace J 12 Substitute K ρ *I x I y , J 21 Subs...

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Abstract

The invention discloses an SPCP infrared weak and small target detection method based on 3DATV constraints and belongs to the field of infrared image processing and target detection. The method comprises the following steps of: step 1, constructing the third-order tensor of an original image; 2, constructing the priori weight tensor of the original image; 3, constructing a target function by usinga tensor kernel function and a tensor l1 norm and combining with the 3DATV constraints, converting an original target detection problem into an SPCP problem, and solving the target function by usingADMM to obtain a background tensor and a target tensor; 4, reconstructing a background image and a target image according to the background tensor and the target tensor; and 5, segmenting the target image and outputting a target detection result. With the method adopted, the defect that an existing method is low in noise robustness, weak in edge inhibition force, poor in complex scene adaptabilityand the like are eliminated; the maintaining capacity of the shapes of targets is enhanced, and the accuracy of target detection is improved.

Description

technical field [0001] The invention belongs to the field of infrared image processing and target detection, in particular to a SPCP infrared weak and small target detection method based on 3DATV constraints. 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) system in It has been widely used in military, civil and other fields. As a basic function of the IRST system, infrared faint 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 clutter, infrared targets are often spotted or dotted, or even ...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06T7/136
CPCG06T7/0002G06T7/10G06T7/136G06T2207/10048G06T2207/20192
Inventor 彭真明张兰丹宋佳晟杨博文胡峻菘曹思颖梅鹤飞刘鉴锋要端翌胡志欣杨春平
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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