Objective

2

With the development of high-density industrial economy

the air quality gradually declines and haze occurs frequently. Haze is an aerosol system formed by the interaction of human daily life and special climate. Large particles in the air could scatter and absorb light

resulting in image collected degradation

which seriously affects image post-analysis. In general

two kinds of algorithms

namely

physical model image defogging and image enhancement defogging

have been adopted to improve the effect of weather factors on image quality. The former is to construct atmospheric scattering model to compensate distortion and obtain a clear image. The common de-fogging algorithms based on the physical model of atmospheric scattering are dark channel prior de-fogging algorithm and polarization imaging de-fogging algorithm and so on. In the last few years

based on the atmospheric scattering model

experts from all over the world have studied the dark channel priori de-fogging. Based on the dark channel priori principle of image de-fogging

on the one hand

the algorithm cannot keep the edge information of the image in the region where the depth of field changes greatly

and a halo phenomenon occurs in the fog-free image; on the other hand

when the global atmospheric light value is close to the pixel luminance component of the foggy image

the color distortion will occur in the restored image. The latter is achieved by improving the quality of image details according to the characteristics of human vision. The representative algorithms include de-fogging algorithm based on histogram equalization

homomorphic filtering

wavelet transform

Retinex theory

and atmospheric modulation transfer function. The basic idea of histogram equalization-based de-fogging algorithm is to obtain uniform distribution of histogram and increase the contrast of the image. The homomorphic filtering based de-fogging algorithm divides the image into irradiating component and reflection component in the frequency domain and increases the contrast of the image by enhancing the high-frequency information of the image. The fog algorithm based on wavelet transform in time domain and frequency domain transformation locally can effectively extract information from the signal. The de-fogging algorithm based on Retinex theory describes color invariance because it has good effect on dynamic range compression

detail enhancement

color fidelity

and so on. The de-fogging algorithm based on the atmospheric modulation transfer function predicts the corresponding upflow transfer function and aerosol transfer function through the formula

obtains the atmospheric modulation transfer function from the product of the two

and then recovers the degraded image in the frequency domain. The attenuation caused by the atmospheric modulation function is compensated. The MSRCR algorithm considers the ratio between the trichromatic channels of the image

so the color distortion should be eliminated to enhance the local detail to a certainextent.However

the time complexity of the algorithm is high

and the operation is complex. However

the following problems persist: the high light region and the thick foggy areas of the images acquired under the fog condition

the inaccurate transmittance calculation often results in detail loss of restored image

halo phenomenon

and contrast and color that cannot meet human visual characteristics. This paper proposes an image defogging algorithm combining GF-MSRCR with dark channel prior.

Method

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Weighted quad tree method is adopted to fast search the minimum channel graph to obtain the global atmospheric light value. The GF-MSRCR algorithm is used to preliminarily estimate the transmittance for the image enhancement. According to the dark channel prior theory

the minimum channel graph is estimated again. The pixel fusion is operated on the two above results with a certain proportion to determine the transmittance estimation value

which is further modified by variation function and by median filtering to acquire the precise transmittance value. Finally

the atmospheric scattering model is used to restore the foggy image and obtain haze-removed image with complete contour and clear details after contrast and color correction.

Result

2

A computer with a lab platform of Intel (R) Core (TM) i5-7300HQ CPU @ 2.50 GHz 8 GB RAM is used

and the lab environment is MATLAB R2015b under Windows 10. Four types of fog sky images including close-range

small part sky area

large sky area and white object scene are defogged. Theoretical and experimental results show that on one hand

more edge information and local details in the image could be preserved by the proposed algorithm; moreover

color could be restored with high fidelity. With the aid of modification of variation function on scene transmittance and the smooth and optimization of median filtering

the algorithm could accurately process bright areas

such as sky

and retain more edge details. After the restoration and adjustment of the close-range image

the contrast and hue are well restored. After the image restoration and adjustment in the small sky area

the visual effect is better. After image restoration and adjustment

the contrast and color of sky region are more natural. After the white object image is restored and adjusted

the clarity and color can satisfy the visual characteristics of the human eye. When the proposed algorithm is applied to an image

the subjective visual effect of fog removal becomes evident. Five evaluation indicators

namely

information entropy

contrast

structural similarity

average gradient

and running time

are used to compare the image defogging quality of different algorithms. In particular

the running time obviously decreases by 53.22% with increases in the information entropy by 7.87%

contrast by 21.95%

average gradient by 47.73%

and structural similarity by 15.58%. The algorithm shows good restoration results for scene images containing fog close shot

a small sky area

a large sky area

or white objects.

Conclusion

2

The image dehazing algorithm fusing GF-MSRCR and dark channel prior could quickly and effectively retain image details

eliminate halo

and satisfy human visual characteristics. The algorithm possesses certain practicability and universality. Future research would capture fog images in more complex scenes and restore foggy images.