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Image defogging method based on linear learning model and smooth morphological reconstruction

A technology for learning models and morphology, applied in image enhancement, image analysis, image data processing, etc., to achieve rich image details, high image clarity, and good dehazing effect.

Pending Publication Date: 2021-09-28
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0014] Purpose of the invention: In order to solve the problem of how to obtain images with good performance from haze images, the present invention proposes an image defogging method based on a linear learning model and smooth morphological reconstruction, using a weighted linear learning model to estimate the ambient light; using Morphological reconstruction retains important structural information in the image processing process; finally, the transfer function value is calculated by grayscale erosion and grayscale expansion, and based on the obtained ambient light and transfer function value, a good performance image can be obtained from the haze image

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  • Image defogging method based on linear learning model and smooth morphological reconstruction
  • Image defogging method based on linear learning model and smooth morphological reconstruction
  • Image defogging method based on linear learning model and smooth morphological reconstruction

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Embodiment Construction

[0072] The technical scheme of the present invention will be further described in conjunction with the accompanying drawings and embodiments.

[0073] It can be seen from the formula (3) that the ambient light needs to be estimated first, and then the transfer function value is calculated, so the present invention proposes the following figure 1 An image defogging method based on a linear learning model and smooth morphological reconstruction is shown, the specific content is:

[0074] According to the atmospheric scattering model (formula (1)), the ambient light component A is constant, but this assumption is unreasonable. Since the ambient light value of the haze scene mainly depends on the local area value of the pixel point, the severe haze The ambient illuminance A value of the image is quite different from the general foggy image. Obviously, the ambient light component A is close to the haze image I, while the value of the transmission map tends to 0. In addition, the ...

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Abstract

The invention discloses an image defogging method based on a linear learning model and smooth morphological reconstruction. The method comprises the following steps: estimating ambient illumination by using a weighted linear learning model; reserving important structure information in the image processing process through morphological reconstruction; and finally, calculating a transmission function value through gray scale corrosion and gray scale expansion, and obtaining an image with good performance from the haze image based on the obtained environment illumination and the transmission function value.

Description

technical field [0001] The invention belongs to the technical field of image defogging, in particular to an image defogging method based on a linear learning model and smooth morphological reconstruction. Background technique [0002] The quality of an outdoor image captured by an image capturing device is easily affected by weather conditions (such as fog or smog) and the shooting distance. Koschmieder (Koschmieder, H. Theorie der horizontalen sichtweite: kontrastund sichtweite. Munich, Germany: Keim & Nemnich, 1925) first proposed an atmospheric scattering model, and expressed the model as: [0003] I(x)=t(x)J(x)+(1-t(x))A (1) [0004] Among them, I(x) is the foggy image, J(x) is the image after dehazing, A represents the illumination component in the environment, t(x) (0<t(x)<1) is the depth weight factor of pixel x . [0005] The transfer function t(x) can be expressed as: [0006] t(x)=e -αd(x) (2) [0007] Among them, d(x) represents the depth scene, α rep...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T5/30
CPCG06T5/30G06T2207/10024G06T5/92G06T5/70
Inventor 庄立运居勇峰杨松王晓晖顾相平
Owner HUAIYIN INSTITUTE OF TECHNOLOGY