Single image rain removal model using pure rain dictionary learning

Abstract

A novel single image rain removal model is proposed. Based on the gradient magnitude and direction of rain streaks, the pure rain region can be extracted from the high-frequency component of rainy image. To ensure proper rain removal, a pure rain dictionary is learned from the extracted pure rain region, and the learned rain dictionary is used to reconstruct the rainy mask from the decomposed high-frequency component. To adaptively remove rain pixels and preserve more details of non-rain pixels, a rainy mask is incorporated into the model. In the proposed model, an improved bilateral filter is only used to handle rain pixels. The experimental results show that the proposed model is superior to existing models in resolving the problems of over-smoothing and rain-streak remains in synthetic and real-world rainy images. Consequently, a better quantitative index and visual quality can be achieved.