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Image defogging method based on linear learning model

A learning model and linear model technology, applied in image enhancement, image analysis, image data processing, etc., can solve problems such as dehazing image oversaturation, and achieve the effect of improving image quality

Active Publication Date: 2021-04-27
HUAIYIN INSTITUTE OF TECHNOLOGY
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  • Claims
  • Application Information

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Problems solved by technology

However, this method produces oversaturation in the dehazed image

Method used

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  • Image defogging method based on linear learning model
  • Image defogging method based on linear learning model
  • Image defogging method based on linear learning model

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Embodiment Construction

[0093] The present invention will be further elaborated below in conjunction with specific embodiments.

[0094] Such as figure 1 Shown is the system block diagram of the present invention, a kind of image defogging method based on linear learning model provided by the present invention, comprises the following steps:

[0095] S1: Use the atmospheric scattering model to dehaze the haze image, namely:

[0096] I(x)=t(x)J(x)+(1-t(x))A (1)

[0097] Among them, I(x) is the foggy image, J(x) is the defogged image, A represents the illumination component in the environment, t(x) (0<t(x)<1) is the depth weight factor of pixel x, and the transfer function t(x) can be expressed as:

[0098] t(x)=e -ad(x) (2)

[0099] Among them, d(x) represents the depth scene, and a represents the atmospheric parameter, which is a constant;

[0100] S2: Use the channel difference (CD) map of each component (R, G, B) of the color haze image to divide the haze image into different sub-blocks:

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Abstract

The invention discloses an image defogging method based on a linear learning model. The method includes calculating parameters in a transmission function value through covariance of three components of a haze color image; secondly, proposing a linear model based on three variables (brightness, saturation and hue) to estimate a depth scene; in order to obtain a coefficient value of the linear model, introducing an iterative algorithm, and training the model by utilizing the haze image; predicting an ambient light value by utilizing a linear model based on polynomial kernel guided filtering; finally, using the algorithm provided by the invention to obtain a defogged image through a physical model.

Description

technical field [0001] The invention relates to the field of image defogging, in particular to an image defogging method based on a linear learning model. Background technique [0002] Outdoor or indoor scenes are often degraded by smog or other tiny particles floating in the air, which is a physical phenomenon. However, the image acquisition equipment used in reality cannot process low-resolution images caused by smog or particles due to technical limitations. As a result, the details of the image captured by the device are blurred, that is, the color decay and the contrast ratio are reduced. Therefore, it is of great significance to study methods and technologies to improve the quality of haze images in many fields, such as aerial images, image classification, image restoration, and image recognition. [0003] According to the references: Zhu, Y.; Min, W.; Jiang, S. Attribute-Guided Feature Learning for Few-Shot Image Recognition. IEEE Transactions on Multimedia. 2020 (E...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/90
CPCG06T7/90G06T2207/10004G06T2207/20081G06T5/73G06T5/70Y02T10/40
Inventor 庄立运王晓晖居勇峰季仁东顾相平
Owner HUAIYIN INSTITUTE OF TECHNOLOGY