  Single image deraining regards an input image as a fusion of a background
image, a transmission map, rain streaks, and atmosphere light. While advanced
models are proposed for image restoration (i.e., background image generation),
they regard rain streaks with the same properties as background rather than
transmission medium. As vapors (i.e., rain streaks accumulation or fog-like
rain) are conveyed in the transmission map to model the veiling effect, the
fusion of rain streaks and vapors do not naturally reflect the rain image
formation. In this work, we reformulate rain streaks as transmission medium
together with vapors to model rain imaging. We propose an encoder-decoder CNN
named as SNet to learn the transmission map of rain streaks. As rain streaks
appear with various shapes and directions, we use ShuffleNet units within SNet
to capture their anisotropic representations. As vapors are brought by rain
streaks, we propose a VNet containing spatial pyramid pooling (SSP) to predict
the transmission map of vapors in multi-scales based on that of rain streaks.
Meanwhile, we use an encoder CNN named ANet to estimate atmosphere light. The
SNet, VNet, and ANet are jointly trained to predict transmission maps and
atmosphere light for rain image restoration. Extensive experiments on the
benchmark datasets demonstrate the effectiveness of the proposed visual model
to predict rain streaks and vapors. The proposed deraining method performs
favorably against state-of-the-art deraining approaches.
