The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning
Open accessAbstract
Learning-based color enhancement approaches typically learn to map from input images to retouched images. Most of existing methods require expensive pairs of input-retouched images or produce results in a non-interpretable way. In this paper, we present a deep reinforcement learning (DRL) based method for color enhancement to explicitly model the step-wise nature of human retouching process. We cast a color enhancement process as a Markov Decision Process where actions are defined as global color adjustment operations. Then we train our agent to learn the optimal global enhancement sequence of the actions. In addition, we present a 'distort-and-recover' training scheme which only requires high-quality reference images for training instead of input and retouched image pairs. Given high-quality reference images, we distort the images' color distribution and form distorted-reference image pairs for training. Through extensive experiments, we show that our method produces decent enhancement results and our DRL approach is more suitable for the 'distort-and-recover' training scheme than previous supervised approaches. Supplementary material and code are available at https://sites.google.com/view/distort-and-recover/.
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
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
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607
Image Aesthetic Assessment: An experimental survey
Deng, Y., Loy, C. C., & Tang, X. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2696576 · Cited by 306 · cited 1×
“There has been a long line of works to assess the aesthetic scores of images as summarized in [2], but there is still no winning method that gives satisfying results.”
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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
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
TreEnhance: A tree search method for low-light image enhancement
Cotogni, M., & Cusano, C. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109249 · Cited by 40
ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing
Santos, C. F. G. D., Arrais, R. R., Silva, J. V. S. D., Silva, M. H. M. D., Neto, W. B. G. D. A., Lopes, L. T., Bileki, G. A., Lima, I. O., Rondon, L. B., Souza, B. M. D., et al. (2025) · ACM Computing Surveys · doi:10.1145/3708516 · Cited by 9
“In the second, results were validated over the MIT-Adobe FiveK dataset [11] and compared with HDRNet [31], DPE [16], White-Box [41], Distort-and-Recover [84], and DeepUPE [117] methods where CURL scored better on PSNR and LPIPS metrics, but DeepUPE scored the best on SSIM.”
Metadata
- Authors
- Park, J., Lee, J.-Y., Yoo, D., & Kweon, I. S.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5928-5936
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798105912
- Cited by
- 211
- References
- 5
- FWCI
- 7.31
- Type
- conference paper
- Language
- en
- Open access
- green
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- Park, J., Lee, J.-Y., Yoo, D., & Kweon, I. S. (2018). Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00621
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