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Visible light and infrared image fusion method based on structure group double sparse learning

An infrared image and fusion method technology, applied in the field of image processing, can solve the problems of poor quality of visible light and infrared image fusion, poor adaptability of correlation dictionary learning, etc., and achieve the effect of obvious fusion effect, reduced complexity, and high application value.

Inactive Publication Date: 2020-04-28
TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0007] In order to solve the problem that the existing sparse representation fusion algorithm ignores the correlation between blocks and the dictionary learning adaptability is poor, resulting in poor fusion quality of visible light and infrared images, it provides a visible light fusion algorithm based on structural group double sparse learning. Fusion method with infrared image

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  • Visible light and infrared image fusion method based on structure group double sparse learning
  • Visible light and infrared image fusion method based on structure group double sparse learning
  • Visible light and infrared image fusion method based on structure group double sparse learning

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Embodiment Construction

[0023] A method for fusion of visible light and infrared images based on structural group double-sparse learning, comprising the following steps:

[0024] S1: Carry out sliding window processing on the input visible light and infrared images, find similar image blocks of the original image blocks, perform group vectorization on the original image blocks and similar image blocks, and establish an image similar structure group matrix;

[0025] S11: Using the sliding window technology, the size of the sliding window is n×n, and the sliding step is 1 pixel, and the visible light image V and the infrared image I of size M×N are divided into (M-n+1)·(N-n +1) image blocks.

[0026] S12: For each original image block p i , in the neighborhood of L×L, using the Euclidean distance as the measurement criterion, calculate the relationship with p i The most similar s image blocks, and the original image block p i Form a similarity group g with s similar image blocks i , there are s+1 i...

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Abstract

The invention relates to a visible light and infrared image fusion method based on structure group double sparse learning. The method comprises the following steps: (1) carrying out sliding window processing on input visible light and infrared images, searching similar blocks of original image blocks, carrying out group vectorization, and establishing an image similar structure group matrix; (2) taking the image similar structure group matrix as a training sample, forming a base dictionary by utilizing a Kronecker product of shear wavelets, obtaining a sparse dictionary through online learning, and performing linear reconstruction on the base dictionary and the sparse dictionary to obtain a final double sparse dictionary; and (3) in combination with the double sparse dictionaries, performing group sparse solution on the image similar structure group by adopting SOMP to obtain a group sparse coefficient, and obtaining a final fused image through image reconstruction by adopting a maximum fusion rule. The method solves the problem that the existing sparse fusion algorithm ignores the correlation between the image blocks, the dictionary adaptability is poor, and the image fusion quality is low, and can be applied to the fields of remote sensing detection, medical diagnosis, intelligent driving, safety monitoring and the like.

Description

technical field [0001] The invention relates to an image fusion method in the field of image processing, in particular to a visible light and infrared image fusion method based on structural group double-sparse learning. Background technique [0002] Visible light and infrared imaging technologies have important applications in remote sensing detection, medical diagnosis, intelligent driving, and safety monitoring. Visible light sensors can describe the scene information of the environment through light reflection imaging, and have high spatial resolution, but are easily affected by lighting conditions and weather changes; infrared sensors can reflect the radiation characteristics of the target and the background through thermal radiation imaging, But the structural features and texture information of the target is missing. The two types of imaging use the different physical characteristics of the target for detection, which is highly complementary. Only by fusing the two t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/50
CPCG06T5/50G06T2207/20221G06T2207/20081G06T2207/10048
Inventor 王志社姜晓林王君尧武圆圆
Owner TAIYUAN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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