基于深度密集连接控制网络的单幅图像去雨

Deep controlled dense connection network for single image deraining

雨线造成的图像质量退化严重影响图像有效应用及计算机视觉算法,因此图像去雨十分必要。目前主流的深度学习去雨方法仅对单一尺寸的雨线有效,并且存在雨线去除不完全、模糊背景等问题。针对以上难点,提出了基于深度密集连接控制网络的单幅图像去雨算法。通过引入多尺度特征网络加强对不同尺寸雨线的提取能力,引入注意力机制模块提升对有雨区域的关注度,引入密集连接控制网络以完整表示雨线特征。实验表明,该方法在合成数据集以及真实数据集对比主流去雨方法效果均有提升。

Image quality degradation caused by rain streaks seriously affects the effective application of image and computer vision algorithm, so image deraining is very necessary. At present, mainstream deraining methods based on deep learning are only effective for single size rain streaks, and there are problems such as incomplete rain streaks removal and fuzzy background. Aiming at these difficulties, a single image deraining algorithm based on deep controlled dense connection network is presented. Through the introduction of multi-scale block, the ability to extract rain streaks of different sizes was enhanced. And attention mechanism module was injected to pay more attention to raining areas. What is more, controlled dense connection block was also introduced to fully represent the rain streaks characteristics. Experiments show that the proposed method outperforms some mainstream methods both on the synthetic dataset and the real dataset.