Image processing algorithms used in surveillance systems are designed to work under good weather conditions. For example, in a rainy day, raindrops are adhered to camera lenses and windshields, resulting in partial occlusions in acquired images, and making performance of image processing algorithms significantly degraded. To improve performance of surveillance systems in a rainy day, raindrops have to be automatically detected and removed from images. Addressing this problem, this paper proposes an adherent raindrop detection method from a single image which does not need training data and special devices. The proposed method employs image segmentation using Maximally Stable Extremal Regions (MSER) and qualitative metrics to detect adherent raindrops from the result of MSER-based image segmentation. Through a set of experiments, we demonstrate that the proposed method exhibits efficient performance of adherent raindrop detection compared with conventional methods.
['Koichi Ito', 'Kazumasa Noro', 'Takafumi Aoki']
An adherent raindrop detection method using MSER