Rain removal from dynamic scene based on motion segmentation

Jie Chen, Lap-Pui Chau

Abstract: Rain removal technique has been intensively studied over these years, the photometric, chromatic, and probabilistic properties of the rain have been exploited to remove the rainy effect. However, current available algorithms only work well with light rain and static scenes, when dealing with heavier rain fall in dynamic scenes, obvious visual degradation will occur especially in motion intensive areas. The proposed algorithm is based on motion segmentation of dynamic scenes. Photometric and chromatic constraints are used for rain detection, motion occlusion information are involved in the adaptive prediction of the rain pixels' original value, using both spatial and temporal neighbor information. Results show the proposed algorithm has a much better performance for rainy scenes with large motion than existing algorithms.