The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Low-Light Image Enhancement with Normalizing Flow
Open accessAbstract
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise reconstruction losses and deterministic processes fail to capture the complex conditional distribution of normally exposed images, which results in improper brightness, residual noise, and artifacts. In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. In this way, the conditional distribution of the normally exposed images can be well modeled, and the enhancement process, i.e., the other inference direction of the invertible network, is equivalent to being constrained by a loss function that better describes the manifold structure of natural images during the training. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., & Loy, C. C. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11021-5_5 · Cited by 4,731
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
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,715
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00185 · Cited by 2,459
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016) · arXiv · Cited by 2,067
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
Kindling the Darkness: A Practical Low-light Image Enhancer
Zhang, Y., Zhang, J., & Guo, X. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350926 · Cited by 1,507
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493
Cited by
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Wang, Y., Yang, W., Chen, X., Wang, Y., Guo, L., Chau, L.-P., Liu, Z., Qiao, Y., Kot, A. C., & Wen, B. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02437 · Cited by 196
Self-citationDancing in the Dark: A Benchmark towards General Low-light Video Enhancement
Fu, H., Zheng, W., Wang, X., Wang, J., Zhang, H., & Ma, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01183 · Cited by 33
Memories are One-to-Many Mapping Alleviators in Talking Face Generation
Tang, A., He, T., Tan, X., Ling, J., & Song, L. (2022) · arXiv · doi:10.48550/arxiv.2212.05005 · Cited by 12
Metadata
- Authors
- Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.-P., & Kot, A.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2022-06-28
- Volume
- 36
- Issue
- 3
- Pages
- 2604-2612
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3199334903
- Cited by
- 533
- References
- 41
- FWCI
- 41.53
- Type
- conference paper
- Language
- en
- Open access
- diamond
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