Learning Dual Convolutional Dictionaries for Image De-raining

Rain removal is a vital and highly ill-posed low-level vision task. While currently existing deep convolutional neural networks (CNNs) based image de-raining methods have achieved remarkable results, they still possess apparent shortcomings: First, most of the CNNs based models are lack of interpretability. Second, these models are not embedded with physical structures of rain streaks and background images. Third, they omit useful information in the background images. These deficiencies result in unsatisfied de-raining results in some sophisticated scenarios. To solve the above problems, we propose a Deep Dual Convolutional Dictionary Learning Network (DDCDNet) for these specific tasks. We firstly propose a new dual dictionary learning objective function, and then unfold it into the form of neural networks to learn prior knowledge from the data automatically. This network tries to learn the rain-streaks layer and the clean background using two dictionary learning networks instead of merely predicting the rain-streaks layer like most of the de-raining methods. To further increase the interpretability and generalization capability, we add sparsity and adaptive dictionary to our network to generate dynamic dictionary for each image based on content. Experimental results reveal that our model possesses outstanding de-raining ability on both synthetic and real-world data sets in terms of PSNR and SSIM as well as visual appearance.