论文标题
使用生成对抗网络从不利天气引起的扭曲的图像中生成清晰的图像
Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks
论文作者
论文摘要
我们提出了一种改善受不利天气条件影响的图像的计算机视觉任务的方法，包括由依从雨滴引起的扭曲。克服将计算机视觉应用于受不利天气条件影响的图像的挑战对于使用RGB摄像机的自动驾驶汽车至关重要。为此，我们培训了一个适当的生成对抗网络，并表明它在图像重建和计算机视觉任务的背景下有效地消除了扭曲的效果。我们表明，对象识别是自动驾驶车辆的至关重要的任务，这完全受到粘附雨滴引起的扭曲和阻塞的损害，并且我们的脱毛模型可以恢复性能。本文中描述的方法可应用于所有不利天气条件。
We presented a method for improving computer vision tasks on images affected by adverse weather conditions, including distortions caused by adherent raindrops. Overcoming the challenge of applying computer vision to images affected by adverse weather conditions is essential for autonomous vehicles utilizing RGB cameras. For this purpose, we trained an appropriate generative adversarial network and showed that it was effective at removing the effect of the distortions, in the context of image reconstruction and computer vision tasks. We showed that object recognition, a vital task for autonomous driving vehicles, is completely impaired by the distortions and occlusions caused by adherent raindrops and that performance can be restored by our de-raining model. The approach described in this paper could be applied to all adverse weather conditions.
