TY - JOUR
T1 - Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems
AU - Wang, Qian
AU - Guo, Cai
AU - Dai, Hong Ning
AU - Xia, Min
N1 - Funding information:
This work was supported in part by the Science and Technology Planning Project of Guangdong Province of China under Grant 2022A1515011551; in part by the Natural Science Foundation of Guangdong Province of China under Grant 2021A1515011091; in part by the Project of Educational Commission of Guangdong Province of China under Grant 2020ZDZX3056, Grant 2021KTSCX07, and Grant 2021KQNCX051; and in part by the Doctor Starting Fund of Hanshan Formal University, China, under Grant QD20190628. The Associate Editor for this article was S. H. A. Shah. (Qian Wang and Cai Guo contributed equally to this work.) (Corresponding author: Hong-Ning Dai.)
Publisher Copyright:
© 2023 IEEE.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - Intelligent transportation systems (ITS)
KW - traffic image processing
KW - image deblurring
KW - variant-depth neural networks
UR - http://www.scopus.com/inward/record.url?scp=85153367985&partnerID=8YFLogxK
U2 - 10.1109/TITS.2023.3255839
DO - 10.1109/TITS.2023.3255839
M3 - Journal article
SN - 1524-9050
VL - 24
SP - 5792
EP - 5802
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 6
ER -
