The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Open accessAbstract
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that translates photos taken by cameras with limited capabilities into DSLR-quality photos automatically. We tackle this problem by introducing a weakly supervised photo enhancer (WESPE) - a novel image-to-image Generative Adversarial Network-based architecture. The proposed model is trained by under weak supervision: unlike previous works, there is no need for strong supervision in the form of a large annotated dataset of aligned original/enhanced photo pairs. The sole requirement is two distinct datasets: one from the source camera, and one composed of arbitrary high-quality images that can be generally crawled from the Internet - the visual content they exhibit may be unrelated. In this work, we emphasize on extensive evaluation of obtained results. Besides standard objective metrics and subjective user study, we train a virtual rater in the form of a separate CNN that mimics human raters on Flickr data and use this network to get reference scores for both original and enhanced photos. Our experiments on the DPED, KITTI and Cityscapes datasets as well as pictures from several generations of smartphones demonstrate that WESPE produces comparable or improved qualitative results with state-of-the-art strongly supervised methods.
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Cites
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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
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
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DehazeNet: An End-to-End System for Single Image Haze Removal
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The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174
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
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
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks
Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., & Lin, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00113 · Cited by 594
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
Low-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Metadata
- Authors
- Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., & Van Gool, L.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 804-80409
- OpenAlex
- W2753918352
- Cited by
- 219
- References
- 10
- FWCI
- 9.62
- Type
- conference paper
- Language
- en
- Open access
- green
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