Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions
Nowadays, rapid advancements in computer vision, image processing, and artificial intelligence (AI) have significantly benefited autonomous vehicles. Visual perception is crucial for enhancing the functionality and safety of self-driving technology. However, adverse weather and illumination conditio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/2/72 |
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| _version_ | 1850078671676637184 |
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| author | Tiande Mo Siqian Zheng Wai-Yat Chan Renhua Yang |
| author_facet | Tiande Mo Siqian Zheng Wai-Yat Chan Renhua Yang |
| author_sort | Tiande Mo |
| collection | DOAJ |
| description | Nowadays, rapid advancements in computer vision, image processing, and artificial intelligence (AI) have significantly benefited autonomous vehicles. Visual perception is crucial for enhancing the functionality and safety of self-driving technology. However, adverse weather and illumination conditions can impair visual capabilities, affecting environmental awareness, decision-making, and safe navigation. This work provides a comprehensive review of AI image enhancement methods and benchmark datasets, including deblurring, deraining, dehazing, and low-light enhancement, along with the integration of multiple image enhancement techniques in computer vision tasks. Specifically, this review focuses on advancements for real-world applications and summarizes performance metrics for real-time operation in automotive vision systems. Furthermore, the paper highlights efforts and challenges in real-world testing to ensure the effectiveness and reliability of these solutions in practical applications, which is essential for enabling autonomous vehicles to operate safely and efficiently under various challenging conditions, thereby contributing to the future of intelligent transportation systems. |
| format | Article |
| id | doaj-art-84649bbdb9a044c0baae3821f7c6da2e |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-84649bbdb9a044c0baae3821f7c6da2e2025-08-20T02:45:30ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-01-011627210.3390/wevj16020072Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather ConditionsTiande Mo0Siqian Zheng1Wai-Yat Chan2Renhua Yang3Hong Kong Productivity Council (HKPC), Kowloon, Hong Kong SAR, ChinaHong Kong Productivity Council (HKPC), Kowloon, Hong Kong SAR, ChinaHong Kong Productivity Council (HKPC), Kowloon, Hong Kong SAR, ChinaHong Kong Productivity Council (HKPC), Kowloon, Hong Kong SAR, ChinaNowadays, rapid advancements in computer vision, image processing, and artificial intelligence (AI) have significantly benefited autonomous vehicles. Visual perception is crucial for enhancing the functionality and safety of self-driving technology. However, adverse weather and illumination conditions can impair visual capabilities, affecting environmental awareness, decision-making, and safe navigation. This work provides a comprehensive review of AI image enhancement methods and benchmark datasets, including deblurring, deraining, dehazing, and low-light enhancement, along with the integration of multiple image enhancement techniques in computer vision tasks. Specifically, this review focuses on advancements for real-world applications and summarizes performance metrics for real-time operation in automotive vision systems. Furthermore, the paper highlights efforts and challenges in real-world testing to ensure the effectiveness and reliability of these solutions in practical applications, which is essential for enabling autonomous vehicles to operate safely and efficiently under various challenging conditions, thereby contributing to the future of intelligent transportation systems.https://www.mdpi.com/2032-6653/16/2/72vehicle safetyconvolutional neural networkartificial intelligenceautonomous vehicleimage enhancementcomputer vision |
| spellingShingle | Tiande Mo Siqian Zheng Wai-Yat Chan Renhua Yang Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions World Electric Vehicle Journal vehicle safety convolutional neural network artificial intelligence autonomous vehicle image enhancement computer vision |
| title | Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions |
| title_full | Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions |
| title_fullStr | Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions |
| title_full_unstemmed | Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions |
| title_short | Review of AI Image Enhancement Techniques for In-Vehicle Vision Systems Under Adverse Weather Conditions |
| title_sort | review of ai image enhancement techniques for in vehicle vision systems under adverse weather conditions |
| topic | vehicle safety convolutional neural network artificial intelligence autonomous vehicle image enhancement computer vision |
| url | https://www.mdpi.com/2032-6653/16/2/72 |
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