基于多细节卷积神经网络的单幅图像去雨方法

Multi-detail convolutional neural networks for single image rain removal

  • 摘要: 本文提出一种基于多细节卷积神经网络的单幅图像去雨方法。考虑到雨条信息大都存在于有雨图像的高频部分,所提方法将有雨图像通过引导滤波进行多次分解得到平滑图像和不同频率分布的多细节图像,提出多细节卷积神经网络学习有雨图像和无雨图像之间的映射关系,从而获得无雨图像。考虑到实际收集相同场景下的有雨图像和无雨图像难度较大,本文采用无雨图像和人工合成的有雨图像作为训练数据,而测试部分则采用合成的雨图和真实的雨图。实验结果表明,本文所提方法能够有效去除图像中的雨条信息。

     

    Abstract: This paper, proposes a single image rain removal method based on multi-detail convolutional neural network. Considering that the raindrop information is mostly presented in the high frequency part of the rainy-image, the proposed method exploits the guided filter to decompose the rainy-image into a smooth image and multi-detail images with different frequency distribution, and then a multi-detail convolutional neural network is developed to learn the mapping relationship between the rainy-image and the rainless image, so as to obtain a rainless image. Considering that it is difficult to collect rainy-images and rainless images in the same scene in reality, we use rainless images and artificially synthesized rainy-images as training data, while utilize the synthesized rainy-images and real rainy-images as the testing data. The experimental results have shown that the proposed method can effectively remove the raindrop information from the rainy-image.

     

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