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

Qian Wang, Cai Guo, Hong Ning Dai*, Min Xia

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)5792-5802
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number6
Early online date4 Apr 2023
DOIs
Publication statusPublished - Jun 2023

Scopus Subject Areas

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications

User-Defined Keywords

  • Intelligent transportation systems (ITS)
  • traffic image processing
  • image deblurring
  • variant-depth neural networks

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