Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems

Wang, Qian and Guo, Cai and Dai, Hong-Ning and Xia, Min (2023) Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems, 24 (6): 6. pp. 5792-5802. ISSN 1524-9050

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Abstract

Intelligent transportation systems (ITS) with surveillance cameras capture traffic images or videos. However, images or videos in ITS often encounter blurs due to various reasons. Considering resource limitations, although recent technologies make progress in image-deblurring, there are still challenges in applying image-deblurring models in practical transportation systems: the model size and the running time. This work proposes an artful variant-depth network (VDN) to address the challenges. We design variant-depth sub-networks in a coarse-to-fine manner to improve the deblurring effect. We also adopt a new connection namely stack connection to connect all sub-networks to reduce the running time and model size while maintaining high deblurring quality. We evaluate the proposed VDN with the state-of-the-art (SOTA) methods on several typical datasets. Results on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) show that the VDN outperforms SOTA image-deblurring methods. Furthermore, the VDN also has the shortest running time and the smallest model size.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Intelligent Transportation Systems
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2210
Subjects:
?? computer science applicationsmechanical engineeringautomotive engineeringmechanical engineeringautomotive engineeringcomputer science applications ??
ID Code:
195231
Deposited By:
Deposited On:
05 Jun 2023 13:00
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 23:44