Double fidelity terms unidirectional variation model for single-image rain removal

Abstract

Videos and images captured in poor weather conditions not only have seriously degraded visual quality, but they can also restrict the performance of many computer vision algorithms. One such severe weather condition is rain, which causes complex local intensity fluctuations and fuzzy background images. We study the single-image rain removal to improve the visual quality of images captured in rain. We propose a rain streak removal method with a double fidelity terms unidirectional variational model based on wavelet transform, which is utilized to separate the rain streaks. The rain streaks component is only included in the approximate component and the corresponding high-frequency components. The double fidelity terms unidirectional variational model is then used for removing the rain streaks. The regularization term mainly introduces image-prior information by unidirectional variation. The numerical algorithm is realized by the augmented Lagrange algorithm. Experimental results on a variety of simulated and real rain images show that the proposed method can efficiently remove rain streaks. Experimental results demonstrate the good performance of the proposed model, removing rain streaks while reducing information loss from the background. The proposed method also requires much less computation.