Detecting Blurred Ground-based Sky/Cloud Images
This addresses a specific issue for researchers using sky imagers to study atmospheric events, but it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of detecting blurred images from ground-based sky cameras, which is challenging due to varying cloud features and uniform sky backgrounds, achieving a detection accuracy of 94% using a static external marker.
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.