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Double-supervised image dehazing method, system, medium and equipment based on deep learning

A deep learning, dual-supervision technology, applied in the field of image processing, can solve problems such as affecting the dehazing effect, and achieve the effect of avoiding gradient explosion, avoiding gradient disappearance, and reducing training time.

Active Publication Date: 2021-03-19
JINAN UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When performing image processing based on image restoration, if you do not use external equipment to measure the image depth of field, you need to use methods such as dark channel prior or color decay prior to estimate the image depth of field, but these prior knowledge are based on conventional conditions. The assumption, if you want to deal with haze images that do not meet the assumption conditions, it will affect the effect of defogging

Method used

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  • Double-supervised image dehazing method, system, medium and equipment based on deep learning
  • Double-supervised image dehazing method, system, medium and equipment based on deep learning
  • Double-supervised image dehazing method, system, medium and equipment based on deep learning

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Embodiment 1

[0070] In this embodiment, a double-supervised lightweight image defogging system based on deep learning is provided, including: a foggy image and a fogless image sample acquisition module, a neural network system building module and a neural network system training module;

[0071] The foggy image and fog-free image sample acquisition module is used to acquire foggy image and fog-free image samples, and the fog-free image samples are used as label comparison samples;

[0072] Such as figure 1 As shown, the neural network system building block is used to construct the neural network system, and the neural network system includes a down-sampling module, a feature extraction module and an up-sampling module;

[0073] Downsampling module: The downsampling module includes a convolutional layer and a maximum pooling layer. The convolutional layer extracts image features, and then uses the maximum pooling operation. Its function is to reduce the size of the image, so that subsequent...

Embodiment 2

[0084] Such as figure 2 As shown, the present embodiment provides a double-supervised image defogging method based on deep learning, comprising the following steps:

[0085] S1: Obtain foggy images and tag non-foggy images;

[0086] S2: Build a neural network system, initialize the convolution kernel weights of the neural network to a Gaussian random distribution, and set the deviation value; in this embodiment, initialize the convolution kernel weight w in the neural network to a mean value of 0 and a variance of Gaussian random distribution of 0.05, the deviation b is set to a constant of 0.1;

[0087] The specific construction steps of the neural network system are as follows:

[0088] S21: Downsampling: Extract image features through the convolutional layer, extract the most significant features of the image through the maximum pooling layer, and reduce the image size, so that subsequent network calculations can be completed faster;

[0089] S22: Feature extraction: ad...

Embodiment 3

[0130] This embodiment also provides a storage medium, the storage medium may be a storage medium such as ROM, RAM, magnetic disk, optical disk, etc., and the storage medium stores one or more programs. When the programs are executed by the processor, the implementation of Embodiment 2 based A double-supervised image defogging method for deep learning, the method includes the following steps:

[0131] S1: Obtain foggy images and tag non-foggy images;

[0132] S2: Build a neural network system, initialize the convolution kernel weights of the neural network to a Gaussian random distribution, and set the deviation value; in this embodiment, initialize the convolution kernel weight w in the neural network to a mean value of 0 and a variance of Gaussian random distribution of 0.05, the deviation b is set to a constant of 0.1;

[0133] The specific construction steps of the neural network system are as follows:

[0134] S21: Downsampling: Extract image features through the convol...

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Abstract

The invention discloses a dual-supervised lightweight image defogging method, system, medium and equipment based on deep learning. The method steps are: obtaining foggy images and labeling fog-free images; constructing a neural network system; training the neural network system ; The hazy image is input into the neural network system to obtain the first transmission graph, and the hazy image is subjected to the image restoration algorithm to obtain the second transmission graph; the first and second transmission graphs are subjected to mean square error calculation to obtain the loss function L t ; The first transmission map is subjected to the inverse operation of the atmospheric scattering model to obtain the dehazed image; the loss function L is obtained by comparing the dehazed image with the labeled haze-free image. d ;L t and L d Combine according to the set ratio to obtain L total ; The foggy image is input into the trained neural network system to obtain the dehazed image; the system includes a foggy and haze-free image sample acquisition module, a neural network system building module and a neural network system training module. The network parameter magnitude of the present invention is small , less training time, can avoid subjective settings and enhance the defogging effect.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a double-supervised lightweight image defogging method, system, medium and equipment based on deep learning. Background technique [0002] With the intensification of environmental pollution, haze weather is becoming more and more frequent. Due to the scattering effect of suspended particles in the air (such as fog, haze, etc.), bad weather not only leads to low visibility, but also images taken in hazy weather often have degradation problems such as low contrast, color shift, and poor visual effects. . Most outdoor vision systems need to extract image features clearly and accurately, and the degradation of image quality will affect the effectiveness of subsequent computer vision tasks. [0003] In the early research, the method based on image enhancement was adopted, which only improved the contrast of the haze image without considering the physical model. Th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T2207/20081G06T2207/20084G06T5/73
Inventor 李展陈昱铃黄维健钟锐彬张建航
Owner JINAN UNIVERSITY
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