通过深度引导的对抗性学习重新思考雨水清除的混合物
Yongzhen Wang1, Xuefeng Yan2, Yanbiao Niu3
1School of Computer Science and Technology, Anhui University of Technology, Ma'anshan, 243032, China.
概括
本研究介绍了DEMore-Net,这是一种新的深度学习模型,用于从图像中删除复杂的雨 (MOR) 工件混合物. 通过整合深度估计,它显著提高了雨降低的图像质量,在恶劣的天气条件下提高了可见度.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 雨天严重降低了室外图像的可见性,原因是像雨滴,条纹和雾这样的工件.
- 现有的图像脱轨方法因有限的工件处理而与雨混合物 (MOR) 斗争.
- 降解随着距离的变化而变化,使有效的雨水清除复杂化.
研究的目的:
- 提出一个有效的图像脱轨模式,以消除雨混合物 (MOR).
- 开发一个模型,利用场景深度信息来增强脱轨.
- 引入一种新的规范化技术,以提高脱轨的性能.
主要方法:
- 一个联合学习范式,DEMore-Net,集成深度估计和MOR删除.
- 使用场景深度作为区分和移除各种雨水工件的指导.
- 引入混合规范化块 (HNB) 以提高脱轨效率.
主要成果:
- 与现有方法相比,DEMore-Net在去除雨水混合物方面表现出卓越的性能.
- 深度信息有效指导移除各种雨水工件.
- 在合成和现实数据上的实验验验证了模型的有效性.
结论:
- DEMore-Net在解决雨造成的复杂图像退化方面取得了重大进展.
- 整合深度估计是强大的图像脱轨的有希望的方向.
- 拟议的混合规范化区块进一步增强了该模型的脱轨能力.
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