| Summary: | Since the rain line stripes in the image have different shapes and sizes and are unevenly distributed, the rain density of the single neural network learning uneven distribution is weak, and the rain removal effect is not significant. This paper proposes a rain density sensing guide expansion network to remove rain from a single images. The network is divided into two parts. The first part is the rain density perception network classifying the images of different density rains(Heavy rain, Medium rain, Light rain). The second part is the expansion network guided by the joint rain density perception classification information learning different rain density characteristics details for detecting rain lines and removing rain. Experiments show the effectiveness of the method in the de-rain on synthetic and real data sets.
|
|---|