能见度
计算机科学
薄雾
人工智能
计算机视觉
水下
可靠性(半导体)
失真(音乐)
频道(广播)
功率(物理)
带宽(计算)
计算机网络
放大器
气象学
地质学
物理
光学
海洋学
量子力学
作者
Peng Liu,Chufeng Zhang,Qi Hao,Guoyu Wang,Haiyong Zheng
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-12-01
卷期号:23 (12): 25396-25407
被引量:20
标识
DOI:10.1109/tits.2022.3145815
摘要
The vision system is important for almost all kinds of autonomous driving systems. However, visual data interfered by scattering media, such as smoke, haze, water, and other non-uniform media will be degraded seriously, showing the characteristics of detail loss, poor contrast, low visibility, or color distortion. These characteristics can significantly interfere with the reliability of autonomous driving systems. In real environments the image degradation mechanism is complex, and the estimation of degradation parameters is difficult. This issue remains to be solved. In this study, we employed dense blocks as the framework and introduced the attention mechanism to our model from four dimensions: Multi-scale Attention, Channel Attention, Structure Attention, and ROI (region of interest) Attention. With the help of the training data provided by the weakly supervised model, the proposed method achieved excellent performance in the task of scattering medium imaging optimization in different scenes. Comparative experiments show that the proposed method is robust, and is superior to other state-of-the-art methods in image dehazing, and underwater image enhancement tasks. It is of great significance to improve the reliability of autonomous driving systems in underwater and severe weather environments.
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