目的 针对雾天图像高亮和雾浓区域中容易出现场景透射率值求取不准确，导致复原后的图像细节丢失、出现光晕现象、对比度和色彩难以满足人眼的视觉特性等问题，提出了一种融合引导滤波优化的色彩恢复多尺度视网膜算法（GF-MSRCR）和暗通道先验的图像去雾算法。方法 首先利用加权四叉树方法从最小通道图中快速搜索全局大气光值，再从图像增强角度应用GF-MSRCR算法初步估计场景透射率值，依据暗通道先验原理对最小通道图进行二次估测，根据两次求取结果按一定比例进行像素级图像融合，得到场景透射率估计值；利用变差函数修正估计值，经中值滤波进一步优化得到场景透射率的精确值，最后通过大气散射模型恢复雾天图像，调整对比度和恢复颜色后，得到了轮廓完整且细节清晰的无雾图像。结果 理论分析和实验结果表明，经本文算法去雾处理后的图像信息熵、对比度、平均梯度、结构相似性分别平均提升了7.87%、21.95%、47.73%和15.58%，同时运行时间缩短了53.22%，对近景、含小部分天空区域、含大片天空区域和含白色物体场景的多种类型雾天图像显示出较好的复原效果。结论 融合GF-MSRCR和暗通道先验的图像去雾算法能快速有效保留图像的细节信息、消除光晕，满足了人眼的视觉特性，具有一定的实用性以及普适性。

Objective 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 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 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 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.