Per-pixel classification of clouds from whole sky HDR images

Satilmis, Pinar; Bashford-Rogers, Thomas; Chalmers, Alan; Debattista, Kurt

doi:10.1016/j.image.2020.115950

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Domain adaptation is used for training synthetic and real-world datasets. [178] presented a per-pixel classification method for identifying cloud types based on HDR sky images. Their method uses a patch-based feature extraction approach, which extracts the representative feature vectors used for labeling.…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Wang

Yoon

2021

Preprint

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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.

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Section: Applicationsmentioning

confidence: 99%