@Article{cmes.2023.027727,
AUTHOR = {Bingcai Wei, Di Wang, Zhuang Wang, Liye Zhang},
TITLE = {Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1975--1988},
URL = {http://www.techscience.com/CMES/v137n2/53344},
ISSN = {1526-1506},
ABSTRACT = {With the increasing popularity of artificial intelligence applications, machine learning is also playing an increasingly
important role in the Internet of Things (IoT) and the Internet of Vehicles (IoV). As an essential part of the IoV,
smart transportation relies heavily on information obtained from images. However, inclement weather, such as
snowy weather, negatively impacts the process and can hinder the regular operation of imaging equipment and the
acquisition of conventional image information. Not only that, but the snow also makes intelligent transportation
systems make the wrong judgment of road conditions and the entire system of the Internet of Vehicles adverse.
This paper describes the single image snow removal task and the use of a vision transformer to generate adversarial
networks. The residual structure is used in the algorithm, and the Transformer structure is used in the network
structure of the generator in the generative adversarial networks, which improves the accuracy of the snow removal
task. Moreover, the vision transformer has good scalability and versatility for larger models and has a more
vital fitting ability than the previously popular convolutional neural networks. The Snow100K dataset is used for
training, testing and comparison, and the peak signal-to-noise ratio and structural similarity are used as evaluation
indicators. The experimental results show that the improved snow removal algorithm performs well and can obtain
high-quality snow removal images.},
DOI = {10.32604/cmes.2023.027727}
}