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Dark channel prior and edge information-based image defogging method

A dark channel prior and edge information technology, applied in the field of image defogging based on dark channel prior and edge information, can solve the problem of halo in the edge area, achieve good detail information, eliminate influence, and avoid halo Effect

Inactive Publication Date: 2017-08-18
SICHUAN UNIV
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Problems solved by technology

[0005] The present invention provides an image defogging method based on dark channel prior and edge information, which solves the problem of halo in the edge area that cannot be completely solved in the existing image defogging method

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  • Dark channel prior and edge information-based image defogging method
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Embodiment Construction

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] Such as figure 1 As shown, the present invention is a kind of image defogging method based on dark channel prior and edge information, is characterized in that, mainly comprises the following steps:

[0028] (1) Collection of degraded images in foggy days;

[0029] (2) Estimate the value of atmospheric light using techniques based on block processing;

[0030] (3) Sobel operator is used to detect the grayscale image of the degraded image in foggy weather, and the edge information map of the original image is extracted by dilation operation;

[0031] (4) Adaptively select the window size according to the binary image obtained in step (3) to obtain a rough transmittance image;

[0032] (5) Use guided filtering in the gradient domain to refine the transmittance map;

[0033] (6) Correct the transmittance map obtained in step (...

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Abstract

The invention provides a dark channel prior and edge information-based image defogging method, and belongs to the technical field of image defogging methods. The method mainly comprises the following steps of: (1) estimating a value of atmospheric light by adoption of a block-based technology; (2) carrying out edge detection and expansive operation on a fog-degraded image, and extracting edge information of an original foggy image; (3) self-adaptively selecting a window size according to an obtained edge information graph so as to obtain a rough transmissivity graph; (4) refining the transmissivity graph by adoption of a rapid gradient domain steerable filter method, and importing a tolerance coefficient and a correction factor to correct the refined transmissivity graph; and (5) substituting the value of the atmospheric light and the corrected transmissivity graph by utilizing a fog-degraded model, so as to obtain a fog-free image. According to the method provided by the invention, when the transmissivity graph is solved, the edge information of the image is combined to self-adaptively select the window size, so that the halo phenomenon at the edge area can be effectively solved.

Description

technical field [0001] The invention relates to the fields of digital image processing and computer vision, in particular to an image defogging method based on dark channel prior and edge information. Background technique [0002] As we all know, digital image technology has been widely used in security monitoring, transportation, meteorology, surveying and mapping and other fields. However, in the process of collecting images outdoors, the image quality is often affected by the natural environment. Fog is a common weather in our country. In foggy weather, there are some turbid media (water vapor, dust particles, etc.) in the atmosphere. Due to the different optical properties of different media, light is transmitted through these media. Partially absorbed and scattered during the process, the light received by the image acquisition device changes, resulting in a decrease in image clarity, low contrast, blurring, and scene features that cannot be extracted. These defects s...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20024G06T2207/20021G06T2207/20182G06T5/73
Inventor 王明辉刘运王跃李晗杨慧婷侯腾
Owner SICHUAN UNIV
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