Rain streak removal from video sequence using spatiotemporal appearance

Capturing images in the challenging atmosphere for example during rain and snow critically degrades the quality of the images. These external phenomena create low contrast, blur and thereby reduce the visibility of the images. Many computer vision applications like visual traffic surveillance, intelligent vehicles, and entertainments are affected by reduced visibility. Rain streaks removal in a video (RSRV) has significant importance in the outdoor vision systems like surveillance video and has recently been studied comprehensively. In the last few years, many approaches have been made towards solving this problem including matrix decomposition, convolutional neural network, streak orientation modelling, Gaussian mixture modelling (GMM), etc. In this paper, we propose a concise model for RSRV based on the spatiotemporal appearance (STA) of rain streak in a video to exploit the rain streak appearance property. Here we apply Gaussian mixture modelling (GMM) to separate the background and foreground. Then, we use STA to separate the rain streak from moving foreground. The experimental results establish that the proposed method outperforms the state-of-the-art methods significantly for both real and synthetic rains.