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A novel perturbation-based degraded image super-resolution method for object recognition in intelligent transportation system

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成果类型:
期刊论文
作者:
Cheng Zhang;Shan Zeng*;Zhiguang Yang;Yulong Chen;Hao Li;...
通讯作者:
Shan Zeng
作者机构:
[Cheng Zhang; Shan Zeng; Zhiguang Yang; Hao Li] College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
[Yulong Chen] College of Medicine and Health Science, Wuhan Polytechnic University, Wuhan, China
[Yuanyan Tang] Faculty of Science and Technology, University of Macau, Macau, China
通讯机构:
[Shan Zeng] C
College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
语种:
英文
关键词:
Image super-resolution reconstruction;Perturbation mechanism;Intelligent transportation systems;Traffic sign recognition
期刊:
Neural Computing and Applications
ISSN:
0941-0643
年:
2025
卷:
37
期:
6
页码:
5169-5186
基金类别:
NSFC-CAAC#&#&#U1833119 Hubei Provincial Key Research and Development Program#&#&#2023BBB046 Excellent young and middle-aged scientific and technological innovation teams in colleges and universities of Hubei Province#&#&#T2021009 Natural Science Foundation of Hubei Province#&#&#2022CFB925 Knowledge Innovation Program of Wuhan-Shugung Project#&#&#2023010201020460
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
医学与健康学院
摘要:
Intelligent transportation system (ITS) plays an important role in assisting drivers to master road information and optimize traffic flow. However, image degradation resulting from complicated environmental factors, such as motion blur caused by vehicle movement and illumination condition, caused some difficulties in current object recognition research on the ITS that may pose serious risks to driving safety. In order to tackle these challenges, this paper introduces a novel perturbation-based image super-resolution method based on GAN inversion (PSRGANI), utilizing a perturbation mechanism to...

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