Adherent Raindrop Detection and Removal in Video

Shaodi You, Robby T. Tan, Rei Kawakami, Katsushi Ikeuchi; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1035-1042

Abstract


Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatiotemporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.

Related Material


[pdf]
[bibtex]
@InProceedings{You_2013_CVPR,
author = {You, Shaodi and Tan, Robby T. and Kawakami, Rei and Ikeuchi, Katsushi},
title = {Adherent Raindrop Detection and Removal in Video},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}