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2018 | OriginalPaper | Buchkapitel

Deep High Dynamic Range Imaging with Large Foreground Motions

verfasst von : Shangzhe Wu, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep HDR imaging, input images are first aligned using optical flows before merging, which are still error-prone due to occlusion and large motions. In stark contrast to flow-based methods, we formulate HDR imaging as an image translation problem without optical flows. Moreover, our simple translation network can automatically hallucinate plausible HDR details in the presence of total occlusion, saturation and under-exposure, which are otherwise almost impossible to recover by conventional optimization approaches. Our framework can also be extended for different reference images. We performed extensive qualitative and quantitative comparisons to show that our approach produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods, and is robust across various inputs, including images without radiometric calibration.

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Vorheriges Kapitel Open-World Stereo Video Matching with Deep RNN

Nächstes Kapitel Linear Span Network for Object Skeleton Detection

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Titel: Deep High Dynamic Range Imaging with Large Foreground Motions
verfasst von: Shangzhe Wu
Jiarui Xu
Yu-Wing Tai
Chi-Keung Tang
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01215-1

Electronic ISBN: 978-3-030-01216-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01216-8_8

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