Open Access

ARTICLE

Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles

Bingcai Wei, Di Wang, Zhuang Wang, Liye Zhang^*

College of Computer Science and Technology, Shandong University of Technology, Zibo, 255049, China

* Corresponding Author: Liye Zhang. Email:

(This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)

Computer Modeling in Engineering & Sciences 2023, 137(2), 1975-1988. https://doi.org/10.32604/cmes.2023.027727

Received 11 November 2022; Accepted 29 January 2023; Issue published 26 June 2023

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.

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