A method for removing rain in a
video based on
noise modeling is disclosed. Under the assumption of a low-rank background, the rain bar
noise component and the moving foreground in the video are simultaneously estimated. First, video data containing rain
noise is acquired and a model is initialized; a rain map generation model is created according to the characteristics of the rain noise and the video foreground; the structural characteristics of the rain imaging in the video-a rain bar formed by moving rain droplets on each small block in an image is identical in the direction, the small block prior distribution of the rain bar is established; a moving
object detection model is established according to the characteristics of the video foreground sparsity; the model is converted into a
rain removal model under the maximum likelihood
estimation framework; a rain-containing video and the
rain removal model are applied to get a rain-removed video and other statistical variables, and the rain-removed video is output. The method aims to build a high-quality video
rain removal model based on a rain map generation principle and rain bar noise structure characteristics, thereby more accurately allowing the video rain removal technology to be widely applied to complex raining scenes with the moving foreground.