Neural network image defogging method based on hybrid convolution channel attention mechanism and hierarchical learning

A neural network and attention technology, applied in the field of image processing, can solve the problems of unsatisfactory restoration effect and difficult acquisition, and achieve the effect of improving the dehazing performance of the model

Pending Publication Date: 2020-08-14
WENZHOU UNIVERSITY
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However, it is difficult to obtain accurate global atmospheric light and air scattering rate paramete

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  • Neural network image defogging method based on hybrid convolution channel attention mechanism and hierarchical learning
  • Neural network image defogging method based on hybrid convolution channel attention mechanism and hierarchical learning
  • Neural network image defogging method based on hybrid convolution channel attention mechanism and hierarchical learning

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[0047] See Figure 1 to Figure 5 The present invention discloses a method for defogging neural network images based on a hybrid convolutional channel attention mechanism and layered learning, including the following steps:

[0048] S1. Building an image defogging model; wherein the image defogging model includes a multi-scale hierarchical feature extractor, a hybrid convolution channel attention module, and an image reconstruction module;

[0049] The specific process is, such as figure 2 As shown, the image defogging model is constructed. The image defogging model includes a multi-scale hierarchical feature extractor (such as figure 2 Shown), hybrid convolution channel attention module (such as figure 2 Shown) and the image reconstruction module (such as figure 2 Shown).

[0050] S2. Obtain foggy image data, and extract six feature maps of different scales and different depths of the fog map in stages by using the above multi-scale layered extractor;

[0051] The specific proces...

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Abstract

The invention discloses a neural network image defogging method based on a hybrid convolution channel attention mechanism and hierarchical learning. The method comprises the following steps: constructing an image defogging model; acquiring foggy image data, and extracting feature maps of six different scales and different depths of a foggy image by stages by using a multi-scale layered extractor;constructing a hybrid convolution channel attention module based on hybrid convolution and an attention mechanism, and processing the fused features of the six feature maps by the hybrid convolution channel attention module to make a defogging model pay attention to effective features and perform feature defogging; reconstructing the defogged features into a clear fog-free image through an image reconstruction module; and calculating the loss of the restored image and the clear image thereof, and optimizing the image defogging model. According to the technical scheme, effective defogging processing is carried out on the actually shot fog image, and a high-quality fog-free image is recovered.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a neural network image defogging method based on a mixed convolution channel attention mechanism and layered learning. Background technique [0002] In recent years, in outdoor traffic monitoring and other fields, the camera system is often affected by harsh environmental weather such as rain, snow, fog, haze, etc. The pictures captured by the camera system affected by the environment will affect the normal work of monitoring personnel or tracking applications , therefore, it is of great significance to restore the image degraded by the influence of haze and so on. [0003] Image dehazing is one of the key issues in image restoration. The fog map can be modeled using the atmospheric light scattering model. The model is as follows: [0004] I=tJ+A(1-t) [0005] t(x)=e βd(x) [0006] Among them, I is the foggy image, t is the air scattering rate, J is the underlying cl...

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/003G06N3/08G06T2207/20221G06T2207/20081G06T2207/20084G06N3/045Y02T10/40
Inventor 张笑钦王涛王金鑫赵丽
Owner WENZHOU UNIVERSITY
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