The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Fast Underwater Image Enhancement for Improved Visual Perception
Open accessAbstract
In this letter, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP,a large-scale dataset of a paired and an unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available athttps://github.com/xahidbuffon/funie-gan.
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 1×
“We analyze the performance of standard deep visual models for underwater object detection [7], 2D human body-pose estimation [8], and visual attention-based saliency prediction [9]; although results vary depending on the image qualities of a particular test set, on an average, we observe 11-14%, 22-28%, and 26-28% imp…”
Perceptual Generative Adversarial Networks for Small Object Detection
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.211 · Cited by 953 · cited 4×
“Qualitative performance comparison of FUnIE-GAN and FUnIE-GAN-UP with learning-based methods: CycleGAN [17], UGAN-P [6], Pix2Pix [16], LS-GAN [28], Res-GAN [43], and Res-WGAN [26]; the super-scripted asterisk (∗) denotes unpaired training.”
BackgroundMethodInfluentialEnergy-based Generative Adversarial Network
Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · cited 1×
“Energy-based GANs [27] also improve training stability by modeling the discriminator as an energy function, whereas the Least-Squared GAN [28] addresses the vanishing gradients problem by adopting a least-square loss function for the discriminator.”
Method
Cited by
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Jahidul Islam, M., Luo, P., & Sattar, J. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.018 · Cited by 269
Self-citationUnderwater vision enhancement technologies: a comprehensive review, challenges, and recent trends
Zhou, J., Yang, T., & Zhang, W. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03767-y · Cited by 149
Underwater Optical-Sonar Image Fusion Systems
Kim, H.-G., Seo, J., & Kim, S. M. (2022) · Sensors · doi:10.3390/s22218445 · Cited by 29
“FUnIE-GAN [12] Train fast underwater image enhancement GAN (FUnIE-GAN) to learn global content, color, texture, and style information of underwater images”
Background
Metadata
- Authors
- Islam, M. J., Xia, Y., & Sattar, J.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2020-04
- Volume
- 5
- Issue
- 2
- Pages
- 3227-3234
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2925094354
- Cited by
- 1,675
- References
- 10
- FWCI
- 51.50
- Type
- journal article
- Language
- en
- Open access
- green
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: Fast Underwater Image Enhancement for Improved Visual Perception. https://app.cochat.ai/papers/10.1109/lra.2020.2974710
- The paper
- Islam, M. J., Xia, Y., & Sattar, J. (2020). Fast Underwater Image Enhancement for Improved Visual Perception. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2020.2974710
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.
