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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
TensorFlow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.08695 · Cited by 11,822
Contrast Limited Adaptive Histogram Equalization
Zuiderveld, K. (1994) · Graphics Gems · doi:10.1016/b978-0-12-336156-1.50061-6 · Cited by 4,192
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
Land, E. H. (1977) · Scientific American · doi:10.1038/scientificamerican1277-108 · Cited by 2,981
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
Wang, S., Zheng, J., Hu, H.-M., & Li, B. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2261309 · Cited by 1,755
Chen, C., Chen, Q., Xu, J., & Koltun, V. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00347 · Cited by 1,376
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
Adaptive image contrast enhancement using generalizations of histogram equalization
Stark, J. A. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.841534 · Cited by 1,189
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
Fu, X., Zeng, D., Huang, Y., Zhang, X.-P., & Ding, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.304 · Cited by 1,081
Deep bilateral learning for real-time image enhancement
Gharbi, M., Chen, J., Barron, J. T., Hasinoff, S. W., & Durand, F. (2017) · ACM Transactions on Graphics · doi:10.1145/3072959.3073592 · Cited by 800
Shape, Illumination, and Reflectance from Shading
Barron, J. T., & Malik, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377712 · Cited by 656
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Ignatov, A., Kobyshev, N., Timofte, R., & Vanhoey, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.355 · Cited by 582
Learning photographic global tonal adjustment with a database of input/output image pairs
Bychkovsky, V., Paris, S., Chan, E., & Durand, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995413 · Cited by 536
Learning photographic global tonal adjustment with a database of input / output image pairs
Bychkovsky, V., Paris, S., Chan, E., & Durand, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995332 · Cited by 529
Deep joint demosaicking and denoising
Gharbi, M., Chaurasia, G., Paris, S., & Durand, F. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982399 · Cited by 473
Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
Chen, Y.-S., Wang, Y.-C., Kao, M.-H., & Chuang, Y.-Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00660 · Cited by 467
Glassner, A. S. (1990) · Cited by 401
Real-time edge-aware image processing with the bilateral grid
Chen, J., Paris, S., & Durand, F. (2007) · ACM SIGGRAPH 2007 papers · doi:10.1145/1275808.1276506 · Cited by 394
Single Image Layer Separation Using Relative Smoothness
Li, Y., & Brown, M. S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.346 · Cited by 354
A New Low-Light Image Enhancement Algorithm Using Camera Response Model
Ying, Z., Li, G., Ren, Y., Wang, R., & Wang, W. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.356 · Cited by 313
Self-citationA Joint Intrinsic-Extrinsic Prior Model for Retinex
Cai, B., Xu, X., Guo, K., Jia, K., Hu, B., & Tao, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.431 · Cited by 289
Exposure: A White-Box Photo Post-Processing Framework
Hu, Y., He, H., Xu, C., Wang, B., & Lin, S. (2018) · ACM Transactions on Graphics · doi:10.1145/3181974 · Cited by 279
Automatic Photo Adjustment Using Deep Neural Networks
Yan, Z., Zhang, H., Wang, B., Paris, S., & Yu, Y. (2016) · ACM Transactions on Graphics · doi:10.1145/2790296 · Cited by 271
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van Gool, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00112 · Cited by 219
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning
Park, J., Lee, J.-Y., Yoo, D., & Kweon, I. S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00621 · Cited by 211
Automatic Exposure Correction of Consumer Photographs
Yuan, L., & Sun, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_55 · Cited by 168
Chen, J., Adams, A., Wadhwa, N., & Hasinoff, S. W. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982423 · Cited by 131
High-Quality Exposure Correction of Underexposed Photos
Qing, Z., Yuan, G., Xiao, C., Zhu, L., & Zheng, W. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240595 · Cited by 130
Self-citationAesthetic-Driven Image Enhancement by Adversarial Learning
Deng, Y., Loy, C. C., & Tang, X. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240531 · Cited by 129
Image Correction via Deep Reciprocating HDR Transformation
Yang, X. S., Xu, K., Song, Y., Zhang, Q., Wei, X., & Lau, R. W. H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00193 · Cited by 125
Self-citationRecovering Intrinsic Images with a Global Sparsity Prior On Reflectance
Gehler, P., Rother, C., Kiefel, M., et al. (2011) · Cited by 103
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
Rother, C., Kiefel, M., Zhang, L., et al. (2011) · neural information processing systems · Cited by 86
A Learning-to-Rank Approach for Image Color Enhancement
Yan, J., Lin, S. C.-F., Kang, S. B., & Tang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.382 · Cited by 82
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
