GAN-Based Rain Noise Removal From Single-Image Considering Rain Composite Models
GAN-Based Rain Noise Removal From Single-Image Considering Rain Composite Models
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DOI：
10.1109/access.2020.2976761
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
T. Matsui;M. Ikehara
中科院分区：
文献类型：
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作者：
T. Matsui;M. Ikehara
在恶劣的天气条件下，相机拍摄的户外图像或视频可能会受到大雨和大雾的影响。例如，在雨天，由于图像的视觉质量下降，自动驾驶车辆难以确定如何导航。在本文中，我们解决了一个单一的图像雨去除问题（去雨）。与基于视频的方法相比，基于单图像的方法具有挑战性，因为缺乏时间信息。尽管许多现有方法已经解决了这些挑战，但它们存在过度拟合、过度平滑和不自然的色调变化。为了解决这些问题，我们提出了一种基于GAN的去雨方法。通过实验比较确定了最佳的发电机。为了训练生成器，我们从训练数据集中学习雨天图像和残差图像之间的映射。此外，我们还合成了各种雨图像来训练我们的网络。特别是，我们不仅专注于雨条纹的方向和规模，但也下雨的图像合成模型。实验结果表明，该方法适用于大范围的雨天图像。我们的方法在定量和视觉性能方面也比最先进的方法在合成和真实世界的图像上实现了更好的性能。
Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. For example, on a rainy day, autonomous vehicles have difficulty determining how to navigate due to the degraded visual quality of images. In this paper, we address a single-image rain removal problem (de-raining). As compared to video-based methods, single-image based methods are challenging because of the lack of temporal information. Although many existing methods have tackled these challenges, they suffer from overfitting, over-smoothing, and unnatural hue change. To solve these problems, we propose a GAN-based de-raining method. The optimal generator is determined by experimental comparisons. To train the generator, we learn the mapping between rainy and residual images from the training dataset. Besides, we synthesize a variety of rainy images to train our network. In particular, we focus on not only the orientations and scales of rain streaks but also the rainy image composite models. Our experimental results show that our method is suitable for a wide range of rainy images. Our method also achieves better performance on both synthetic and real-world images than state-of-the-art methods in terms of quantitative and visual performances.