  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.
