FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images.We investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation GAN.The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN.The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images.Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

Single foggy image restoration based on spatial correlation analysis of dark channel prior

Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.

Intelligent Speed Limit System for Safe Expressway Driving in Rainy and Foggy Weather Based on Internet of Things

The feature bends and tunnels of mountainous expressways are often affected by bad weather,specif-ically rain and fog,which significantly threaten expressway safety and traffic efficiency.In order to solve this problem,a vehicle–road coordination system based on the Internet of Things(IoT)is developed that can share vehicle–road information in real time,expand the environmental perception range of vehicles,and realize vehicle–road collaboration.It helps improve traffic safety and efficiency.Further,a vehicle–road cooperative driving assistance system model is introduced in this study,and it is based on IoT for improving the driving safety of mountainous expressways.Considering the influence of rain and fog on driving safety,the interaction between rainfall,water film,and adhesion coefficient is analyzed.An intelligent vehicle–road coordination assistance system is constructed that takes in information on weather,road parameters,and vehicle status,and takes the stopping sight distance model as well as rollover and sideslip model as boundary constraints.Tests conducted on a real expressway demonstrated that the assistance system model is helpful in bad weather conditions.This system could promote intelligent development of mountainous expressways.

Performance Analysis and Prediction for a Free-Space Optical Communication System under Foggy Absorption

We analyzed the performance of a freespace optical(FSO)system in this study,considering the combined effects of atmospheric turbulence,fog absorption,and pointing errors.The impacts of atmospheric turbulence and foggy absorption were modeled using the Fisher-Snedecor F distribution and the Gamma distribution,respectively.Next,we derived the probability density function(PDF)and cumulative probability density function of the optical system under these combined effects.Based on these statistical findings,closed-form expressions for various system metrics,such as outage probability,average bit error rate(BER),and ergodic capacity,were derived.Furthermore,we used a deep neural network(DNN)to predict the ergodic capacity of the system,achieving reduced running time and improved accuracy.Finally,the accuracy of the prediction results was validated by comparing them with the analytical results.

Impact of climate variation on fog in China

Using foggy days and mean temperature and relative humidity data of 602 stations from January to December in the period 1961-2003 in China, the relationship between variations of foggy days and temperature and its possible reason for the 43 years were analyzed by regression, correlation and contrastive analysis methods. The results show that the higher （lower） the mean temperature and the lower （higher） the relative humidity correspond to less （more） foggy days, the relationship is the best in the western, northern and eastern Sichuan, Yunnan-Guizhon Plateau, and southeast highland in China. This induces a decrease in relative humidity when the climate becomes warmer, and eventually brings about a decrease in foggy days in China.

Seasonal variations and size distributions of water-soluble ions in atmospheric aerosols in Beijing, 2012

The characteristics of water-soluble ions in airborne particulate matter in Beijing were investigated using ion chromatography. The results showed that the total concentrations of ions were 83.7 ± 48.9 μg/m3 in spring, 54.0 ± 17.0 μg/m3 in summer, 54.1 ± 42.9 μg/m3 in autumn, and 88.8 ± 47.7 μg/m3 in winter, respectively. Furthermore, out of all the ions, NO3-,SO42-and NH4＋accounted for 81.2% in spring, 78.5% in summer, 74.6% in autumn, and 76.3%in winter. Mg2＋and Ca2＋were mainly associated with coarse particles, with a peak that ranged from 5.8 to 9.0 μm. Na＋, NH4＋and Cl-had a multi-mode distribution with peaks that ranged from 0.43 to 1.1 μm and 4.7 to 9.0 μm. K＋, NO3-, and SO42-were mainly associated with fine particles, with a peak that ranged from 0.65 to 2.1 μm. The concentrations of Na＋, K＋,Mg2＋, Ca2＋, NH4＋, Cl-, NO3-and SO42-were 2.69, 2.32, 1.01, 4.84, 16.9, 11.8, 42.0, and 44.1 μg/m3 in particulate matter（PM） on foggy days, respectively, which were 1.4 to 7.3 times higher than those on clear days. The concentrations of these ions were 2.40, 1.66, 0.92, 4.95, 17.5,7.00, 32.6, and 34.7 μg/m3 in PM on hazy days, respectively, which were 1.2–5.7 times higher than those on clear days.