Abstract
Bad weather especially rain drops degrades the perceptual image quality. It is clear that raindrops destroy the visibility of a scene. In steady bad weather, constituent droplets are very small (1–10 μm) and steadily float in the air. Individual detection of these droplets by the camera is very difficult. In dynamic bad weather, constituent droplets are 1000 times larger than those of the steady weather. Due to this large size, these droplets are visible to the video capturing camera which affects the quality of the image. Thus, there is a need for recognition and extraction of rain drops to enhance the image quality. The objective of this paper is to identify the location of the damage sample as precisely as possible and to remove the detected rain pixels from the image in order to get a clearer and brighter image. In this paper, we present a hybrid approach to identify and extract raindrops from the rainy image in order to restore the image with its original back ground. The algorithm is framed in order to recognize the rain droplets using clustering and shape modeling of raindrops. Proposed framework is based on K-means clustering and Gaussian filter for the efficient retrieval of rain droplets from the rainy image. The k-means clustering results in highest correct clustering rate and Hough transform is used to detect and remove the raindrops from single image. The proposed system showed better results than available approaches for raindrop recognition and deletion.
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Susmitha, A., Dash, L., Alamuru, S. (2020). Recognition and Extraction of Rain Drops in an Image Using Hough Transform. In: Mohanty, M., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2774-6_13
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DOI: https://doi.org/10.1007/978-981-15-2774-6_13
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