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://dx.doi.org/10.1109/cvpr42600.2020.00172
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/papers/Liu_Single-Image_HDR_Reconstruction_by_Learning_to_Reverse_the_Camera_Pipeline_CVPR_2020_paper.pdf,
https://arxiv.org/pdf/2004.01179,
https://academic.microsoft.com/#/detail/3034628923
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Published on 01/01/2020

Volume 2020, 2020
DOI: 10.1109/cvpr42600.2020.00172
Licence: CC BY-NC-SA license

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