Description

Many state-of-the-art computer vision algorithms are designed to work in well-posed visibility conditions. Though this assumption may hold for most indoor and simulation scenarios, environmental noise can badly influence their performance in outdoor settings, e.g. in surveillance applications or in driver assistance systems where a camera is mounted behind the windshield of a moving vehicle. However, proper operation in the presence of rain is a security-relevant prerequisite to many applications, particularly on board mobile vehicles. For example image registration accuracy declines in the presence of raindrops on the windshield due to mismatched features. In this proposed work, a method that automatically detects and removes adherent raindrops. 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 by using the Matlab tool.