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Night haze image defogging method based on dark channel prior and color correction

A dark channel prior and color correction technology, which is applied in the field of computer image processing, can solve problems such as blurred details and distortion of processing results, and achieve the effects of increasing image brightness, reducing image distortion, and high signal-to-noise ratio

Inactive Publication Date: 2016-06-01
TIANJIN UNIV
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  • Claims
  • Application Information

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Problems solved by technology

Through this model, the fog and haze can be removed to a large extent, and the image visibility can be increased, but the processing results have defects such as local distortion and blurred details.

Method used

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  • Night haze image defogging method based on dark channel prior and color correction
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  • Night haze image defogging method based on dark channel prior and color correction

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Embodiment Construction

[0063] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the described specific embodiments are only for explaining and illustrating the present invention, and are not intended to limit the present invention.

[0064] The processing methods for foggy images at night in the prior art are usually:

[0065] Image dehazing based on dark channel prior is generally considered to be the most effective image dehazing method at present, Dong method [14] Low-light image enhancement is achieved based on defogging the inverted image of low-light images.

[0066] According to the atmospheric scattering model:

[0067] I=Jt+A(1-t)(1)

[0068] Among them, I is the foggy image, J is the clear image, t is the transmittance, and A is the background light intensity.

[0069] According to the dark channel prior theory [2] , the transmittance of the foggy image is:

[0070] ...

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Abstract

The invention discloses a night haze image defogging method based on dark channel prior and color correction, comprising steps of inversing a nigh foggy image to obtain an inversion image, using a dark channel prior algorithm to perform defogging on the inversion image and then inversing the inversion image again to obtain a brightness enhancement image, combining with and utilizing a bright enhancement image to obtain a reference image through guiding filtering to obtain a weak halation image I1, utilizing the mean value and the variance corresponding to the RGB channel of the weak halation image I1 to obtain a color correction image I2, utilizing the inversion image of the reference image to perform halo removal on the color correction image I2 to obtain an image I3, performing color correction to obtain an image I4, performing fusion on the I2 and the I4 to obtain the image having no the halo and the color cast, using the dark channel prior based on the local neighborhood to perform de-fogging, and obtaining the output image through the guiding filtering. The invention not only increases the brightness, the contrast ratio, and the average gradient, but is excellent in aspects of highlighting the image details and reducing the image distortion.

Description

technical field [0001] The invention relates to a computer image processing method, in particular to a night image haze removal method based on dark channel prior and color correction. Background technique [0002] Dehazing images of haze at night has an important impact on target recognition, video surveillance, remote sensing monitoring, intelligent transportation and other fields in night scenes. Nighttime haze image defogging technology is generally divided into two parts: nighttime image defogging and low-light image enhancement. [0003] Most of the existing image defogging technologies are aimed at daytime scenes, such as methods based on depth information [1] , a method based on prior information [2] Etc., the effect of defogging is better, but it is invalid for night scenes. Existing low-light image enhancement algorithms mainly include histogram equalization, Retinex algorithm, etc. [3] , these algorithms improve the brightness, contrast, saturation, etc. of th...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/73
Inventor 杨爱萍白煌煌王南杨苏辉王建
Owner TIANJIN UNIV
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