Adherent Raindrop Modeling, Detection and Removal in Video

作者：You Shaodi^*; Tan Robby T; Kawakami Rei; Mukaigawa Yasuhiro; Ikeuchi Katsushi

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(9): 1721-1733.

DOI：10.1109/TPAMI.2015.2491937

• Outdoor vision
• rainy scenes
• raindrop detection
• raindrop removal

Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Modeling, 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 spatio-temporal derivatives of raindrops. To accomplish the idea, we first model adherent raindrops using law of physics, and detect raindrops based on these models in combination with motion and intensity temporal derivatives of the input video. Having detected the raindrops, we remove them and restore the images based on an analysis that some areas of raindrops completely occludes the scene, and some other areas occlude only partially. For partially occluding areas, we restore 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 derivative. For completely occluding areas, we recover them by using a video completion technique. Experimental results using various real videos show the effectiveness of our method.

• 出版日期2016-9