Detecting Blurred Ground-based Sky/Cloud Images

• URL: http://arxiv.org/abs/2110.09764v1
• Date: Tue, 19 Oct 2021 06:51:31 GMT
• Title: Detecting Blurred Ground-based Sky/Cloud Images
• Authors: Mayank Jain, Navya Jain, Yee Hui Lee, Stefan Winkler, and Soumyabrata Dev
• Abstract summary: We propose an efficient framework that can identify the blurred sky/cloud images. Using a static external marker, our proposed methodology has a detection accuracy of 94%.
• Score: 5.178207785968322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ground-based whole sky imagers (WSIs) are being used by researchers in various fields to study the atmospheric events. These ground-based sky cameras capture visible-light images of the sky at regular intervals of time. Owing to the atmospheric interference and camera sensor noise, the captured images often exhibit noise and blur. This may pose a problem in subsequent image processing stages. Therefore, it is important to accurately identify the blurred images. This is a difficult task, as clouds have varying shapes, textures, and soft edges whereas the sky acts as a homogeneous and uniform background. In this paper, we propose an efficient framework that can identify the blurred sky/cloud images. Using a static external marker, our proposed methodology has a detection accuracy of 94\%. To the best of our knowledge, our approach is the first of its kind in the automatic identification of blurred images for ground-based sky/cloud images.

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