Images captured in rainy days suffer from noticeable degradation of scene visibility.Unmanned aerial vehicles(UAVs),as important outdoor image acquisition systems,demand a proper rain removal algorithm to improve visu...Images captured in rainy days suffer from noticeable degradation of scene visibility.Unmanned aerial vehicles(UAVs),as important outdoor image acquisition systems,demand a proper rain removal algorithm to improve visual perception quality of captured images as well as the performance of many subsequent computer vision applications.To deal with rain streaks of different sizes and directions,this paper proposes to employ convolutional kernels of different sizes in a multi-path structure.Split attention is leveraged to enable communication across multiscale paths at feature level,which allows adaptive receptive field to tackle complex situations.We incorporate the multi-path convolution and the split attention operation into the basic residual block without increasing the channels of feature maps.Moreover,every block in our network is unfolded four times to compress the network volume without sacrificing the deraining performance.The performance on various benchmark datasets demonstrates that our method outperforms state-of-the-art deraining algorithms in both numerical and qualitative comparisons.展开更多
基金the Fundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics(No.kfjj20191601).
文摘Images captured in rainy days suffer from noticeable degradation of scene visibility.Unmanned aerial vehicles(UAVs),as important outdoor image acquisition systems,demand a proper rain removal algorithm to improve visual perception quality of captured images as well as the performance of many subsequent computer vision applications.To deal with rain streaks of different sizes and directions,this paper proposes to employ convolutional kernels of different sizes in a multi-path structure.Split attention is leveraged to enable communication across multiscale paths at feature level,which allows adaptive receptive field to tackle complex situations.We incorporate the multi-path convolution and the split attention operation into the basic residual block without increasing the channels of feature maps.Moreover,every block in our network is unfolded four times to compress the network volume without sacrificing the deraining performance.The performance on various benchmark datasets demonstrates that our method outperforms state-of-the-art deraining algorithms in both numerical and qualitative comparisons.
