The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Low-Light Image Enhancement via a Deep Hybrid Network
Abstract
Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and the salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder-decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. The experimental results show that the proposed network favorably performs against the state-of-the-art low-light image enhancement algorithms.
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Cites
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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
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
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Self-citationDeep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
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Cited by
Deep Learning on Image Denoising: An overview
Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091
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
Xiao, J., Fu, X., Liu, A., Wu, F., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183612 · Cited by 389
Shen, Z., Wang, W., Lu, X., Shen, J., Ling, H., Xu, T., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00567 · Cited by 359
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset
Lv, F., Li, Y., & Lu, F. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01466-8 · Cited by 319
Metadata
- Authors
- Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2019-09
- Volume
- 28
- Issue
- 9
- Pages
- 4364-4375
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2935842779
- Cited by
- 508
- References
- 54
- FWCI
- 25.36
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019). Low-Light Image Enhancement via a Deep Hybrid Network. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2019.2910412
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