Image defogging method and system based on deep learning neural network

A neural network and deep learning technology, applied in the field of image defogging methods and systems based on deep learning neural networks, can solve problems that cannot be directly used in the defogging process

Inactive Publication Date: 2018-03-02
CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI
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Problems solved by technology

However, none of these methods can be directly utilized in the dehazing process of a single image

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  • Image defogging method and system based on deep learning neural network
  • Image defogging method and system based on deep learning neural network
  • Image defogging method and system based on deep learning neural network

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[0054] First introduce the theoretical background of the present invention: in order to describe the information of the fog map, the atmospheric scattering model can be written in the following form:

[0055] I(x)=J(x)t(x)+α(1-t(x)) (1)

[0056] t(x)=e -βd(x) (2)

[0057] Among them, I(x) is the observed fog map, J(x) is the real image that needs to be restored, t(x) is the medium transmittance, α is the global atmospheric light constant, and x represents the fog map I(x) For each pixel in , β is the atmospheric scattering coefficient, and d(x) is the depth of field. In formula (1), there are three unknown parameters. After estimating t(x) and α, the real scene graph J(x) can be recovered. Equation (2) shows that t(x) tends to 0 as d(x) tends to infinity. Comprehensive (1) (2) can be obtained.

[0058] α=I(x), d(x)→∞ (3)

[0059] In the actual process, d(x) cannot tend to infinity, but a small transmittance t will be generated at a long distance 0 . Different from the...

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Abstract

The invention discloses an image defogging method and a system based on a deep learning neural network. The method comprises the following steps of inputting an image with fog into a deep learning neural network system; using the deep learning neural network system to carry out characteristic extraction on the image with fog, and carrying out autonomous learning and extracting a fog correlation characteristic; carrying out multiscale mapping on the image with fog, extracting the characteristic of the image with fog in a concentrative mode under different scales and forming a characteristic graph; carrying out local extremum on each pixel in the characteristic graph, maintaining a resolution to be unchanged and acquiring the processed image; carrying out nonlinear regression operation on the processed image and acquiring initial transmissivity t(x); using a guided filter to optimize the transmissivity and carrying out image smoothing processing on the processed image; calculating an atmospheric light parameter; and according to the initial transmissivity t(x) and the atmospheric light parameter, recovering a fogless image. In the invention, connection is established between the system and an existing defogging method, and under the condition that efficiency and easy implementation are guaranteed, compared with the existing method, the method has better defogging performance.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image defogging method and system based on a deep learning neural network. Background technique [0002] Fog is a common atmospheric phenomenon. The ubiquity of dust, smoke or other particles in the air reduces the clarity of the atmosphere. In the field of scene photography, the transmittance of light in the atmosphere is particularly important for the imaging of distant objects, so fog usually brings many problems to imaging, such as: when the light is scattered by particles in the atmosphere, the object is in the visual field when imaging A decrease in contrast in effect. Therefore, the dehazing work has great application prospects in the field of photography and computer vision. [0003] Because the transmission degree of fog in the atmosphere is often related to the depth of field, in different positions, the greater the depth, the thicker the fog....

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
CPCG06T5/003G06T2207/20081G06T2207/20084
Inventor 任航宋玉龙郭巳秋刘博超
Owner CHANGCHUN INST OF OPTICS FINE MECHANICS & PHYSICS CHINESE ACAD OF SCI
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