The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Deep reverse tone mapping
Abstract
Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images ) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.
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Cites
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Image-to-Image Translation with Conditional Adversarial Networks
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Cited by
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
Cai, J., Gu, S., & Zhang, L. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2794218 · Cited by 1,234
Attention-Guided Network for Ghost-Free High Dynamic Range Imaging
Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00185 · Cited by 313
Metadata
- Authors
- Endo, Y., Kanamori, Y., & Mitani, J.
- Venue
- ACM Transactions on Graphics
- Published
- 2017-11-20
- Volume
- 36
- Issue
- 6
- Pages
- 1-10
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2769930525
- Cited by
- 331
- References
- 50
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- closed
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