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Deep learning infrared image denoising method and system based on multi-head self-attention mechanism

An infrared image and deep learning technology, applied in the field of deep learning infrared image denoising, can solve the problems of high noise and poor imaging quality, and achieve the effect of overcoming high noise

Pending Publication Date: 2022-04-26
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The primary purpose of the present invention is to provide a deep learning infrared image denoising method based on the multi-head self-attention mechanism, aiming at the problems of poor imaging quality and high noise in the infrared thermal imaging system, improving the imaging quality and reducing the noise in the infrared image Noise is expected to be widely used in scientific research, military detection, fire monitoring, fault diagnosis, medical analysis and remote sensing

Method used

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  • Deep learning infrared image denoising method and system based on multi-head self-attention mechanism
  • Deep learning infrared image denoising method and system based on multi-head self-attention mechanism
  • Deep learning infrared image denoising method and system based on multi-head self-attention mechanism

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Embodiment 1

[0070] This embodiment provides a deep learning infrared image denoising method based on a multi-head self-attention mechanism, such as figure 1 shown, including the following steps:

[0071] S1: Collect high-definition infrared images and perform preprocessing;

[0072] S2: Obtain a data set according to the high-definition infrared image and the preprocessed infrared image;

[0073] S3: Establish the infrared denoising neural network based on the multi-head self-attention mechanism, use the data set to train the infrared denoising neural network, and obtain the trained infrared denoising neural network;

[0074] S4: Use the trained infrared denoising neural network to denoise the noisy infrared image.

[0075] The multi-head self-attention mechanism was originally used in natural language processing tasks. Because of its powerful global feature extraction and local feature extraction capabilities, it has achieved good results in vision tasks instead of convolutional neural...

Embodiment 2

[0083] On the basis of embodiment 1, this embodiment specifically discloses:

[0084] The infrared denoising neural network based on the multi-head self-attention mechanism in the step S3, such as figure 2 As shown, specifically:

[0085] The infrared denoising neural network based on the multi-head self-attention mechanism includes a local feature extraction module 101, a global feature extraction module 102 and an image recovery module 103, wherein the local feature extraction module 101 extracts the local feature information I in the infrared image local , the global feature extraction module 102 extracts the global feature information I in the infrared image global , the image restoration module 103 takes the local feature information I local and global feature information I global After fusion, it is restored to a noise-free infrared image I rec .

[0086] The local feature extraction module 101, such as image 3 As shown, specifically:

[0087] The local feature ...

Embodiment 3

[0116] A deep learning infrared image denoising system based on multi-head self-attention mechanism, such as Figure 8 shown, including:

[0117] Image collection and processing module, described image collection and processing module is used for collecting high-definition infrared image and carries out preprocessing;

[0118] A data set module, the data set module is used to obtain a data set according to the high-definition infrared image and the preprocessed infrared image;

[0119] A multi-head self-attention denoising neural network module, the multi-head self-attention denoising neural network module is used to establish an infrared denoising neural network based on a multi-head self-attention mechanism, and the infrared denoising neural network is processed by using the data set Carry out training to obtain the trained infrared denoising neural network;

[0120] Denoising module, described denoising module utilizes the infrared denoising neural network trained to deno...

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Abstract

The invention discloses a deep learning infrared image denoising method and system based on a multi-head self-attention mechanism. The method comprises the following steps: S1, collecting and preprocessing a high-definition infrared image; s2, obtaining a data set according to the high-definition infrared image and the preprocessed infrared image; s3, establishing an infrared denoising neural network based on a multi-head self-attention mechanism, and training the infrared denoising neural network by using the data set to obtain a trained infrared denoising neural network; and S4, denoising the infrared image with noise by using the trained infrared denoising neural network. According to the invention, through a multi-head self-attention mechanism deep learning infrared image denoising technology, the problem of high noise in a classic infrared imaging system can be effectively solved. The method is very beneficial to research of an infrared imaging multi-head self-attention mechanism deep learning image denoising technology.

Description

technical field [0001] The present invention relates to the field of infrared imaging, more specifically, to a deep learning infrared image denoising method and system based on a multi-head self-attention mechanism. Background technique [0002] As a means of detection to aid the human visual system's sensitivity to infrared radiation, an infrared sensor outputs an infrared image that reflects temperature differences of objects in the scene. Infrared sensors utilize passive imaging and operate around the clock. Therefore, they are widely used in scientific research, military detection, fire monitoring, fault diagnosis, medical analysis and remote sensing. In general, due to factors including infrared imaging technology principles, external environment distortion, and thermal motion of the sensor itself, infrared images will have problems such as low resolution, blurred edges, loss of details, low image contrast, and background noise. In order to avoid noise from becoming a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/08G06T2207/10048G06T2207/20081G06T2207/20084G06N3/045G06T5/70
Inventor 程良伦吴文昊吴衡
Owner GUANGDONG UNIV OF TECH
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