The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Underexposed Photo Enhancement Using Deep Illumination Estimation
Abstract
This paper presents a new neural network for enhancing underexposed photos. Instead of directly learning an image-to-image mapping as previous work, we introduce intermediate illumination in our network to associate the input with expected enhancement result, which augments the network's capability to learn complex photographic adjustment from expert-retouched input/output image pairs. Based on this model, we formulate a loss function that adopts constraints and priors on the illumination, prepare a new dataset of 3,000 underexposed image pairs, and train the network to effectively learn a rich variety of adjustment for diverse lighting conditions. By these means, our network is able to recover clear details, distinct contrast, and natural color in the enhancement results. We perform extensive experiments on the benchmark MIT-Adobe FiveK dataset and our new dataset, and show that our network is effective to deal with previously challenging images.
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Cites
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Contrast Limited Adaptive Histogram Equalization
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LIME: Low-Light Image Enhancement via Illumination Map Estimation
Guo, X., Li, Y., & Ling, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639450 · Cited by 2,998
The Retinex Theory of Color Vision
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LLNet: A deep autoencoder approach to natural low-light image enhancement
Lore, K. G., Akintayo, A., & Sarkar, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.008 · Cited by 1,951
Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images
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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
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Cited by
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
Li, C., Guo, C., & Chen, C. L. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3063604 · Cited by 719
SNR-Aware Low-light Image Enhancement
Xu, X., Wang, R., Fu, C.-W., & Jia, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01719 · Cited by 688
Self-citationLow-Light Image and Video Enhancement Using Deep Learning: A Survey
Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.-M., Gu, J., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126387 · Cited by 657
Low-Light Image Enhancement with Normalizing Flow
Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.-P., & Kot, A. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20162 · Cited by 533
Learning Enriched Features for Fast Image Restoration and Enhancement
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., & Shao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3167175 · Cited by 531
Metadata
- Authors
- Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., & Jia, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 6842-6850
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948354154
- Cited by
- 983
- References
- 35
- FWCI
- 32.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., & Jia, J. (2019). Underexposed Photo Enhancement Using Deep Illumination Estimation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00701
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