Research On Rain Detection And Removal In Single Image

Computer vision systems are widely used in intelligent transportation, industrial production, target recognition and other fields. However, due to the effect of outdoor raining weather, quality of the captured image is not suitable for high-level image recognition tasks, such as object detection, object recognition, target tracking. Therefore, it is necessary to pre-processing these images so as to get removing the effect of rain drops. In this thesis, we focuses mainly on the pro-processing of images with rain drops, which includes two key components, i.e., rain drop detection and rain drop removing. Towards this end, two approaches are reported in this thesis, which are list as follows:(1) A fast and simple algorithm is proposed for accurately detecting raindrops in a single image. In a single image rain removal, raindrops localization is a key step. We successfully changes the raindrop detection into a classification problem. By collecting and labeling a reliable raindrops database, we obtain a reliable training and testing datasets. After that, the proposed raindrop detection model is trained against the training data, and a raindrop detector is constructed. The experimental results show the proposed raindrop detecting algorithm can achieve a higher accuracy on raindrop detection than the previous methods.(2) An image block reconstruction method is presented to reconstruct the raindrops area. After the raindrops detection, this paper successfully applied the raindrops location information to a single image rain removal. The reason of failure was analyzed through experiment. On this basis, we remove raindrops in single images by combine raindrop position information and image completion algorithm, The experimental results show that the proposed method indeed remove the raindrops from both the subjective and objective aspects.