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

Autor: Lin Wang, Kuk-Jin Yoon
Rok vydání: 2022
Předmět:
Diagnostic Imaging
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image processing
Field (computer science)
Machine Learning (cs.LG)
Computer graphics
Deep Learning
Artificial Intelligence
Human–computer interaction
Image Processing
Computer-Assisted
Computer Graphics
FOS: Electrical engineering
electronic engineering
information engineering
High dynamic range
Point (typography)
business.industry
Applied Mathematics
Deep learning
Image and Video Processing (eess.IV)
Electrical Engineering and Systems Science - Image and Video Processing
Computational Theory and Mathematics
Computer Vision and Pattern Recognition
Artificial intelligence
State (computer science)
business
Algorithms
Software
Zdroj: IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:8874-8895
ISSN: 1939-3539
0162-8828
DOI: 10.1109/tpami.2021.3123686
Popis: 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.
Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), main and suppl. material
Databáze: OpenAIRE
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