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Rain removal in images/videos is still an important task in computer visionfield and attracting attentions of more and more people. Traditional methodsalways utilize some incomplete priors or filters (e.g. guided filter) to removerain effect. Deep learning gives more probabilities to better solve this task.However, they remove rain either by evaluating background from rainy imagedirectly or learning a rain residual first then subtracting the residual toobtain a clear background. No other models are used in deep learning basedde-raining methods to remove rain and obtain other information about rainyscenes. In this paper, we utilize an extensively-used image degradation modelwhich is derived from atmospheric scattering principles to model the formationof rainy images and try to learn the transmission, atmospheric light in rainyscenes and remove rain further. To reach this goal, we propose a robustevaluation method of global atmospheric light in a rainy scene. Instead ofusing the estimated atmospheric light directly to learn a network to calculatetransmission, we utilize it as ground truth and design a simple but noveltriangle-shaped network structure to learn atmospheric light for every rainyimage, then fine-tune the network to obtain a better estimation of atmosphericlight during the training of transmission network. Furthermore, more efficientShuffleNet Units are utilized in transmission network to learn transmission mapand the de-raining image is then obtained by the image degradation model. Bysubjective and objective comparisons, our method outperforms the selectedstate-of-the-art works.

Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes