基于编码—解码结构的多阶段图像去雨滴方法

针对附着镜头或玻璃表面的雨滴会造成图像退化的问题，提出了一种多阶段渐进式图像去雨滴方法。整个去雨滴过程被分解为多个更易于实现的阶段。首先在每个阶段设计多尺度融合的编码—解码网络以学习雨滴特征，通过构建带有门控循环单元的多尺度扩张卷积来细化内部传递的空间特征。然后引入无降维的通道注意力机制对特定空间特征下的通道信息进行提取。最后为加强每个阶段各部分之间的信息交换，采用跨阶段特征融合机制，在每个阶段的编码—解码网络之间加入横向连接，以实现特征信息的横向传递。在每个阶段之间加入监督注意模块，以增强不同阶段之间的信息传递，最终渐进地实现雨滴的去除。实验表明该方法能够有效地去除雨滴。

Multi-stage image raindrop removal via encoder-decoder network

For the problem of image degradation caused by raindrops attached to the lens or glass surface, this paper proposed a multi-stage progressive image raindrop removal method which divides the raindrop removal process into multiple stages to easily implement. Firstly, the multi-scale fused encoder-decoder network at each stage designed to learn raindrop features. The constructed multi-scale dilated convolutions with gated recurrent units refined the internally transferred spatial features. In addition, the no dimensionality reduction channel attention module extracted channel information under specific spatial features. Finally, this paper adopted cross-stage feature fusion mechanism to strengthen the information exchange between the parts of each stage. Adding horizontal connections between the encoder-decoder networks in each stage to realize the horizontal transfer of feature information. And it added a supervised attention module to enhance the information transmission between different stages. The raindrop removal is progressively achieved. Experiments show that the proposed method can effectively remove raindrops.