  Rain removal in images/videos is still an important task in computer vision
field and attracting attentions of more and more people. Traditional methods
always utilize some incomplete priors or filters (e.g. guided filter) to remove
rain effect. Deep learning gives more probabilities to better solve this task.
However, they remove rain either by evaluating background from rainy image
directly or learning a rain residual first then subtracting the residual to
obtain a clear background. No other models are used in deep learning based
de-raining methods to remove rain and obtain other information about rainy
scenes. In this paper, we utilize an extensively-used image degradation model
which is derived from atmospheric scattering principles to model the formation
of rainy images and try to learn the transmission, atmospheric light in rainy
scenes and remove rain further. To reach this goal, we propose a robust
evaluation method of global atmospheric light in a rainy scene. Instead of
using the estimated atmospheric light directly to learn a network to calculate
transmission, we utilize it as ground truth and design a simple but novel
triangle-shaped network structure to learn atmospheric light for every rainy
image, then fine-tune the network to obtain a better estimation of atmospheric
light during the training of transmission network. Furthermore, more efficient
ShuffleNet Units are utilized in transmission network to learn transmission map
and the de-raining image is then obtained by the image degradation model. By
subjective and objective comparisons, our method outperforms the selected
state-of-the-art works.
