High-Dynamic-Range Imaging for Cloud Segmentation
This addresses cloud segmentation for sky-camera images, an incremental improvement in a domain-specific application.
The paper tackles the problem of cloud segmentation in ground-based sky images, which suffer from large luminance dynamic range causing over- and under-exposure, by proposing HDRCloudSeg using HDR imaging based on multi-exposure fusion, achieving very good results.
Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg -- an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.