## 1 Introduction  
Meteorological visibility can be used to indicate the transparency of air and quality of atmosphere and this parameter is an important indicator for sea and air transport [1]. The accuracy of the visibility measurement is affected by a number of external factors such as air lights, object available, light scatter and absorption and suspended particles in the air [2, 3]. The classical approach for Meteorological Optical Range (MOR) [4] measurement includes the manual evaluation method in which visual observation of largest visible distance by well-trained meteorological observer. The accuracy of manual evaluation method is subjected the number of reference targets available in the environment [5] and the judgement of observer [6]. Another popular visibility measurement method is the visibility meter method in which the meter estimates the visible distance by measurement of atmospheric transmittance or extinction coefficient [5]. The forward scattering and back-scattering method [7] are among the most popular metering methods while most visibility meter employs the forward scattering method as it can give reasonable accuracy and performance with lower cost. However, forward scattering visibility meter with high accuracy is very expensive and requires specialized installation and calibration skills and usually this type of meter could give good accuracy with relatively short range only. Therefore, there are many past research work for the visibility estimation based on digital image analysis methods. With the advancement of image processing and machine learning methods, this approach can give reasonable accuracy with relatively lower cost. [PERSON] [8] proposed a new approach to restore visibility of outdoor digital images in presence of haze, fog and sandstorms. [PERSON] [9] proposed a novel deep neural networks method for enhancing visibility under foggy conditions. [PERSON] [10] presented a deep network for dehazing and to enhance the image quality. [PERSON] [11] proposed a fast single image defogging technique and an Atmospheric Scattering Model (ASM) to overcome the problems of non-uniform illumination and multiple scattering. [PERSON] [12] proposed a regression model to forecast the visibility in the Urumqi International Airport. [PERSON] [13] proposed an intelligent digital method to estimate the visibility by Generalized Regression Neural Network (GRNN). [PERSON] [14] proposed a novel Radial Basis Function (RBF) neural network method to eliminate the haze and retain the visible structure's edges and the image brightness. [PERSON] [15] proposed a deep learning method with feature extraction and Support Vector Machine (SVM) to enhance safer driving under foggy weather. [PERSON] [16] proposed a Deep Hybrid Convolutional Neural Network (DHCNN) to estimate visibility under heavy fog. [PERSON] [17] propose a deep learning method to estimate the relative atmospheric visibility from digital images. [PERSON] [18] proposed a new method to estimate the visibilities from digital images of Closed-circuit television (CCTV) under sea fog. [PERSON] [19] proposed a Multi-scale Convolutional Neural Networks to remove the haze of the image. [PERSON] [20] proposed a hierarchical sparse representation method to estimate the image visibility. [PERSON] [21] proposed a Deep Convolutional Neural Networks (DCNN) method to estimate the visibility with insufficient visibility labeled data. [PERSON] [22] proposed a use AlexNet Deep Convolution Neural Networks (DCNN) and Support Vector Machine (SVM) classifier to estimate visibility under foggy weather. [PERSON] [23] proposed a visibility prediction method by using multimodal fusion. [PERSON] [24] reviewed the latest research results on visibility estimation. [PERSON] [25] proposed to use Multiple Support Vector Regression (MSVR) model and deep learning method to estimate visibility. [PERSON] [26] has proposed to use cameras to monitoring visibility impairment. [PERSON] [27] studied the impact of precipitation evaporation on the atmospheric aerosol distribution.