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Single image defogging method based on context-guided generative adversarial network

A single image and context technology, applied in the field of image processing, can solve the problems of poor quality of defogged images, achieve clear defogged results, rich semantic information, and simple operation of the network structure

Pending Publication Date: 2020-12-11
XIAN UNIV OF TECH
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Problems solved by technology

[0010] The purpose of the present invention is to provide a single image defogging method based on context-guided generative confrontation network, which solves the problem of poor quality of defogged images in the prior art

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  • Single image defogging method based on context-guided generative adversarial network
  • Single image defogging method based on context-guided generative adversarial network
  • Single image defogging method based on context-guided generative adversarial network

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Embodiment Construction

[0054] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0055] A single image dehazing method based on context-guided generative adversarial networks, such as figure 1 shown, including the following steps:

[0056] Step 1. Obtain an image data set, normalize the pixel value of each foggy image in the image data set to [-1, 1], and crop it to 256x256x3 to obtain a training set;

[0057] Step 2. Build a generative adversarial network model. The generative adversarial network model includes a generative network and a discriminant network. The generative network includes a feature extraction encoder, a context information extraction decoder and a fusion decoder.

[0058] The feature extraction encoder is used to extract the shallow features and deep features of the foggy image layer by layer;

[0059] The first part of the feature extraction encoder takes the foggy image as input, and extracts the s...

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Abstract

The invention discloses a single image defogging method based on a context-guided generative adversarial network, the generative adversarial network comprises a feature extraction encoder, a context information extraction decoder and a fusion decoder, and the method comprises steps of capturing deep features extracted by the feature extraction encoder through a pyramid decoder. Extracting contextual information implied in the deep features to obtain a rough defogged image; and fusing the image features acquired by the feature extraction encoder and the context information extraction decoder through the fusion decoder to obtain a fine defogged image with excellent quality. The generative adversarial network model is simple in network structure operation and easy to implement; iterative optimization is carried out on parameters of a context information extraction decoder and a fusion decoder through different loss functions, semantic information of a fusion network is enriched, and a clearer defogging result is generated.

Description

technical field [0001] The invention belongs to the technical field of image processing methods, and relates to a single image defogging method based on a context-guided generative confrontation network. Background technique [0002] In hazy weather, images captured outdoors usually suffer from severe degradation such as poor contrast and color distortion. This will bring great difficulties to further image perception and understanding. [0003] Haze is a typical atmospheric phenomenon in which dust, smoke and other particles greatly reduce the quality and visibility of captured images, making further perception and understanding difficult. Therefore, haze removal, especially single image haze removal, has high practicality and practicality, and has extensive academic and industrial value. [0004] Under fog and haze weather conditions, the scattering effect of suspended particles in the atmosphere reduces the contrast and image quality of outdoor collected images, which s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06N3/084G06T2207/20081G06T2207/20084G06N3/045G06T5/73
Inventor 石争浩周昭润
Owner XIAN UNIV OF TECH
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