  Rain removal from a single image is a challenge which has been studied for a
long time. In this paper, a novel convolutional neural network based on wavelet
and dark channel is proposed. On one hand, we think that rain streaks
correspond to high frequency component of the image. Therefore, haar wavelet
transform is a good choice to separate the rain streaks and background to some
extent. More specifically, the LL subband of a rain image is more inclined to
express the background information, while LH, HL, HH subband tend to represent
the rain streaks and the edges. On the other hand, the accumulation of rain
streaks from long distance makes the rain image look like haze veil. We extract
dark channel of rain image as a feature map in network. By increasing this
mapping between the dark channel of input and output images, we achieve haze
removal in an indirect way. All of the parameters are optimized by
back-propagation. Experiments on both synthetic and real- world datasets reveal
that our method outperforms other state-of- the-art methods from a qualitative
and quantitative perspective.
