rain removal in single image system using cnn with guided and l0-smoothing filters

In this work, a robust rain removal algorithm is proposed for removing rain from still images. the algorithm uses a deep network architecture called derainnet for effective rain removal. the proposed network directly learns the mapping relationship between rainy and clean image detail layers from the given set of data. in order to modify the objective function and also to improve the deraining process, other deep cnn based architecture increases the width or depth of the neurons, which in turn increases the complexity of the network. but this work makes use of the image processing domain knowledge which reduces the complexity of the network. instead of training the entire image, only the detail layer of the image is trained. the detailed layer of the image is obtained using two low-pass filters one after the other. they are guided filter and l0-smoothing filter. the results obtained prove that the proposed network performs better deraining on images in comparison to paper [2] with light rain streaks. python version 3.8 is used for this work.