  It is challenging to remove rain-steaks from a single rainy image because the
rain steaks are spatially varying in the rainy image. This problem is studied
in this paper by combining conventional image processing techniques and deep
learning based techniques. An improved weighted guided image filter (iWGIF) is
proposed to extract high frequency information from a rainy image. The high
frequency information mainly includes rain steaks and noise, and it can guide
the rain steaks aware deep convolutional neural network (RSADCNN) to pay more
attention to rain steaks. The efficiency and explain-ability of RSADNN are
improved. Experiments show that the proposed algorithm significantly
outperforms state-of-the-art methods on both synthetic and real-world images in
terms of both qualitative and quantitative measures. It is useful for
autonomous navigation in raining conditions.
