The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Exposure: A White-Box Photo Post-Processing Framework
Abstract
Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this article a deep learning approach that is instead trained on unpaired data, namely, a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits rather than just a “black-box” result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Research works printed but not linked: 40
- Martín Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S. Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Ian Goodfellow , Andrew Harp , Geoffrey Irving , Michael Isard , Yangqing Jia , Rafal Jozefowicz , Lukasz Kaiser , Manjunath Kudlur , Josh Levenberg , Dan Mané , Rajat Monga , Sherry Moore , Derek Murray , Chris Olah , Mike Schuster , Jonathon Shlens , Benoit Steiner , Ilya Sutskever , Kunal Talwar , Paul Tucker , Vincent Vanhoucke , Vijay Vasudevan , Fernanda Viégas , Oriol Vinyals , Pete Warden , Martin Wattenberg , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved January 2017 from http://tensorflow.org/. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved January 2017 from http://tensorflow.org/.
- Martin Arjovsky Soumith Chintala and Léon Bottou. 2017. Wasserstein GAN. arXiv:1701.07875. Martin Arjovsky Soumith Chintala and Léon Bottou. 2017. Wasserstein GAN. arXiv:1701.07875. · arXiv
- 10.1109/CVPR.2011.5995413 · DOI · Paper page
- 10.1145/2010324.1965000 · DOI
- 10.1145/2856400.2856417 · DOI
- 10.1109/ICCV.2009.5459473 · DOI
- Alexei A. Efros and Thomas K. Leung. 1999. Texture synthesis by non-parametric sampling . In Proceedings of the IEEE International Conference on Computer Vision (ICCV’99) , Vol. 2 . 1033--1038. Alexei A. Efros and Thomas K. Leung. 1999. Texture synthesis by non-parametric sampling. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’99), Vol. 2. 1033--1038.
- 10.1145/2647868.2654979 · DOI
- Hui Fang and Meng Zhang . 2017 . Creatism: A deep-learning photographer capable of creating professional work. arXiv:1707.03491. Hui Fang and Meng Zhang. 2017. Creatism: A deep-learning photographer capable of creating professional work. arXiv:1707.03491. · arXiv
- 10.1109/38.988747 · DOI · Paper page
- 10.1145/3072959.3073592 · DOI · Paper page
- Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative adversarial nets . In Proceedings of Advances in Neural Information Processing Systems (NIPS’14) . 2672--2680. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of Advances in Neural Information Processing Systems (NIPS’14). 2672--2680.
- 10.1145/1531326.1531372 · DOI
- 10.1145/2835487 · DOI · Paper page
- 10.1145/2980179.2980254 · DOI · Paper page
- 10.1145/2508363.2508371 · DOI
- Yuanming Hu , Baoyuan Wang , and Stephen Lin . 2017 . FC4: Fully convolutional color constancy with confidence-weighted pooling . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17) . 4085--4094. Yuanming Hu, Baoyuan Wang, and Stephen Lin. 2017. FC4: Fully convolutional color constancy with confidence-weighted pooling. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17). 4085--4094.
- 10.1007/978-3-642-33718-5_41 · doi:10.1007/978-3-642-33718-5_41
- Diederik Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization . In Proceedings of the International Conference on Learning Representations (ICLR’15) . Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR’15).
- Christian Ledig , Lucas Theis , Ferenc Huszar , Jose Caballero , Andrew Cunningham , Alejandro Acosta , Andrew Aitken , Alykhan Tejani , Johannes Totz , Zehan Wang , and Wenzhe Shi . 2016 . Photo-realistic single image super-resolution using a generative adversarial network . In Proceedings of Advances in Neural Information Processing Systems (NIPS’16) . Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. 2016. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of Advances in Neural Information Processing Systems (NIPS’16).
- 10.1109/CVPR.2016.271 · DOI
- 10.1007/978-3-319-46487-9_43 · DOI · Paper page
- Ming-Yu Liu and Oncel Tuzel . 2016 . Coupled generative adversarial networks . In Proceedings of Advances in Neural Information Processing Systems (NIPS’16) . 469--477. Ming-Yu Liu and Oncel Tuzel. 2016. Coupled generative adversarial networks. In Proceedings of Advances in Neural Information Processing Systems (NIPS’16). 469--477.
- 10.1145/2661229.2661277 · DOI
- 10.1145/2766910 · DOI
- 10.1145/2897824.2925881 · DOI · Paper page
- 10.1145/3099564.3099567 · DOI
- Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014 . Stochastic backpropagation and approximate inference in deep generative models . In Proceedings of the International Conference on Machine Learning. Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the International Conference on Machine Learning.
- 10.1109/CVPR.2017.241 · DOI · Paper page
- David Silver , Guy Lever , Nicolas Heess , Thomas Degris , Daan Wierstra , and Martin Riedmiller . 2014 . Deterministic policy gradient algorithms . In Proceedings of the 31st International Conference on Machine Learning (ICML’14) . 387--395. David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014. Deterministic policy gradient algorithms. In Proceedings of the 31st International Conference on Machine Learning (ICML’14). 387--395.
- Richard S. Sutton , David A. McAllester , Satinder P. Singh , and Yishay Mansour . 2000 . Policy gradient methods for reinforcement learning with function approximation . In Proceedings of Advances in Neural Information Processing Systems (NIPS’00) . 1057--1063. Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems (NIPS’00). 1057--1063.
- Hsiao-Yu Fish Tung , Adam W. Harley , William Seto , and Katerina Fragkiadaki . 2017 . Adversarial inverse graphics networks: Learning 2D-to-3D lifting and image-to-image translation from unpaired supervision . In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17) , Vol. 2 . Hsiao-Yu Fish Tung, Adam W. Harley, William Seto, and Katerina Fragkiadaki. 2017. Adversarial inverse graphics networks: Learning 2D-to-3D lifting and image-to-image translation from unpaired supervision. In Proceedings of the IEEE International Conference on Computer Vision (ICCV’17), Vol. 2.
- 10.1145/2010324.1964959 · doi:10.1145/2010324.1964959
- 10.1007/978-3-319-46493-0_20 · DOI · Paper page
- 10.23919/TST.2017.8195348 · DOI · Paper page
- 10.1109/CVPR.2014.382 · DOI
- 10.1007/s11263-015-0801-5 · DOI
- 10.1145/2790296 · DOI · Paper page
- Lantao Yu , Weinan Zhang , Jun Wang , and Yong Yu . 2017 . SeqGAN: Sequence generative adversarial nets with policy gradient . In Proceedings of the AAAI Conference. 2852--2858 . Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI Conference. 2852--2858.
- Richard Zhang , Phillip Isola , and Alexei A. Efros . 2016. Colorful image colorization . In Proceedings of the European Conference on Computer Vision. 649--666 . Richard Zhang, Phillip Isola, and Alexei A. Efros. 2016. Colorful image colorization. In Proceedings of the European Conference on Computer Vision. 649--666.
Cited by
Underexposed Photo Enhancement Using Deep Illumination Estimation
Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., & Jia, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00701 · Cited by 983
Low-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
MAXIM: Multi-Axis MLP for Image Processing
Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00568 · Cited by 616
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
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment
Wang, R., Xu, X., Fu, C.-W., Lu, J., Yu, B., & Jia, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00956 · Cited by 145
Metadata
- Authors
- Hu, Y., He, H., Xu, C., Wang, B., & Lin, S.
- Venue
- ACM Transactions on Graphics
- Published
- 2018-04-30
- Volume
- 37
- Issue
- 2
- Pages
- 1-17
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2963466723
- Cited by
- 279
- References
- 13
- FWCI
- 14.58
- Type
- journal article
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Exposure: A White-Box Photo Post-Processing Framework. https://app.cochat.ai/papers/10.1145/3181974
- The paper
- Hu, Y., He, H., Xu, C., Wang, B., & Lin, S. (2018). Exposure: A White-Box Photo Post-Processing Framework. ACM Transactions on Graphics. https://doi.org/10.1145/3181974
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.