Two-Stage Feature Disentanglement Network for Single Image Rain Removal

Existing single image de-raining methods generally fail to balance the relationship between the detail restoration of rain-free background image and the removal of rain streaks. In this work, we proposed a single image de-raining method based on two-stage features disentanglement network from coarse to fine progressively. Firstly, we constructed the squeeze and excitation residual module to separate the background image and rainy components roughly. Then, we devised a novel context feature fusion and introduced a disentanglement module to decouple the feature relationship between rain streaks and the background image for a fine-grained de-rained image. Besides, we devised a composite loss function to train our network model for the restoration of high-quality rain-free images using Reconstruction loss function, SSIM loss function, Edge loss function and Texture loss function. Comparative experiments on Test100 dataset showed that our method has a peak signal to noise ratio value of 25.57dB, a structural similarity value of 0.89. On 100 real rain rain images, a natural image quality evaluator value of 3.53 and a blind/referenceless image spatial quality evaluator value of 20.16 were obtained. On target segmentation task of RefineNet, the mean intersection over union and the mean pixel accuracy of proposed method were 29.41% and 70.06%, respectively. The proposed method shows clear visual results with more features of background image preserved for image rain removal and the assist of the downstream target segmentation task.