Scipediacontent: Scipediacontent moved page Draft Content 930862988 to Chen et al 2020a

2021-02-01T22:13:33Z

Scipediacontent moved page Draft Content 930862988 to Chen et al 2020a

Scipediacontent: Created page with " == Abstract == Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed reg..."

2021-02-01T22:13:28Z

Created page with " == Abstract == Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed reg..."

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== Abstract ==

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We then propose to learn three specialized CNNs to reverse these steps. By decomposing the problem into specific sub-tasks, we impose effective physical constraints to facilitate the training of individual sub-networks. Finally, we jointly fine-tune the entire model end-to-end to reduce error accumulation. With extensive quantitative and qualitative experiments on diverse image datasets, we demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.

Comment: CVPR 2020. Project page: https://www.cmlab.csie.ntu.edu.tw/~yulunliu/SingleHDR Code: https://github.com/alex04072000/SingleHDR

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/2004.01179 http://arxiv.org/abs/2004.01179]

* [http://arxiv.org/pdf/2004.01179 http://arxiv.org/pdf/2004.01179]

* [http://xplorestaging.ieee.org/ielx7/9142308/9156271/09157653.pdf?arnumber=9157653 http://xplorestaging.ieee.org/ielx7/9142308/9156271/09157653.pdf?arnumber=9157653],
: [http://dx.doi.org/10.1109/cvpr42600.2020.00172 http://dx.doi.org/10.1109/cvpr42600.2020.00172]

* [https://dblp.uni-trier.de/db/conf/cvpr/cvpr2020.html#LiuLCK0CH20 https://dblp.uni-trier.de/db/conf/cvpr/cvpr2020.html#LiuLCK0CH20],
: [https://arxiv.org/abs/2004.01179 https://arxiv.org/abs/2004.01179],
: [https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.html https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.html],
: [https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.pdf https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.pdf],
: [https://arxiv.org/pdf/2004.01179 https://arxiv.org/pdf/2004.01179],
: [https://academic.microsoft.com/#/detail/3034628923 https://academic.microsoft.com/#/detail/3034628923]

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Chen et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 930862988 to Chen et al 2020a

Scipediacontent: Created page with " == Abstract == Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed reg..."