A Hybrid Approach for Detection and Removal of Raindrops Using k-means Clustering and Hough Transformation

The vision system hired Various tasks such as steering: information gathering can be unfavorably exaggerated by ruthless climate conditions such as rain, haze. Rainy weather condition raindrops twig to a frame glass can substantially abolish the scene visibility. Spotting and eliminating rain drops assist many computer visualization bids. To address this problem we introduced hybrid approach for raindrops detection and removal from video. In our approach we use K-means clustering. canny edge detector. Hough transformation and hybrid Otsu plus median filter for the effective reclamation of Rain drops from the video. We use Otsu algorithm at the detection phase and the median filter at the removal phase for fast retrieval of the raindrop from the image. Segmentation of image is perform by k-means clustering results in highest correct clustering rate. In detection process, we implement the Otsu algorithm and Hough transformation algorithm to know the exact location of the raindrop and edge detection is clone by using canny edge detector. The detected region is filtered by using median filter method. The estimation of performance is clone by using the parameters like accuracy and time.