Detecting Blurred Ground-based Sky/Cloud Images

TL;DR

This paper introduces a novel framework for accurately detecting blurred sky/cloud images captured by ground-based sky imagers, achieving high detection accuracy and addressing a key challenge in atmospheric image analysis.

Contribution

The paper presents the first automatic method specifically designed to identify blurred sky/cloud images, improving preprocessing for atmospheric research.

Findings

Detection accuracy of 94% using the proposed framework

First approach for automatic blur detection in ground-based sky images

Effective differentiation between cloud textures and sky background

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.