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pro vyhledávání: '"Wang, YongZhen"'
Autor:
Wang, Yongzhen
The geopolitical tensions between China and the US have dramatically reshaped the American scientific workforce's landscape. To gain a deeper understanding of this circumstance, this study selects the discipline of computer science as a representativ
Externí odkaz:
http://arxiv.org/abs/2411.15907
Haze severely degrades the visual quality of remote sensing images and hampers the performance of road extraction, vehicle detection, and traffic flow monitoring. The emerging denoising diffusion probabilistic model (DDPM) exhibits the significant po
Externí odkaz:
http://arxiv.org/abs/2405.09083
Adverse weather conditions often impair the quality of captured images, inevitably inducing cutting-edge object detection models for advanced driver assistance systems (ADAS) and autonomous driving. In this paper, we raise an intriguing question: can
Externí odkaz:
http://arxiv.org/abs/2403.04443
Visual-based measurement systems are frequently affected by rainy weather due to the degradation caused by rain streaks in captured images, and existing imaging devices struggle to address this issue in real-time. While most efforts leverage deep net
Externí odkaz:
http://arxiv.org/abs/2307.09728
Rainy weather significantly deteriorates the visibility of scene objects, particularly when images are captured through outdoor camera lenses or windshields. Through careful observation of numerous rainy photos, we have found that the images are gene
Externí odkaz:
http://arxiv.org/abs/2303.17766
Recent diffusion models have exhibited great potential in generative modeling tasks. Part of their success can be attributed to the ability of training stable on huge sets of paired synthetic data. However, adapting these models to real-world image d
Externí odkaz:
http://arxiv.org/abs/2301.09430
Small targets are often submerged in cluttered backgrounds of infrared images. Conventional detectors tend to generate false alarms, while CNN-based detectors lose small targets in deep layers. To this end, we propose iSmallNet, a multi-stream densel
Externí odkaz:
http://arxiv.org/abs/2210.16561
Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy scenarios. Besides, they commonly give deterministic dehazed images while
Externí odkaz:
http://arxiv.org/abs/2210.16057
Autor:
Wang, Yongzhen, Yan, Xuefeng, Zhang, Kaiwen, Gong, Lina, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the
Externí odkaz:
http://arxiv.org/abs/2209.01373
Autor:
Wang, Jie, Wang, Yongzhen, Feng, Yidan, Gong, Lina, Yan, Xuefeng, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic str
Externí odkaz:
http://arxiv.org/abs/2209.00977