Low illumination underwater light field images reconstruction using deep convolutional neural networks
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The same data augmentation techniques were applied across all models, with additional preprocessing for noisy images.
“The same data augmentation techniques were applied across all models, with additional preprocessing for noisy images [18, 19].”
From Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search (2026) · cites this paper as [19]
Cites
A flexible new technique for camera calibration
Zhang, Z. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888718 · Cited by 15,004 · see citing works
Light field image denoising using a linear 4D frequency-hyperfan all-in-focus filter
Dansereau, D. G., Bongiorno, D. L., Pizarro, O., & Williams, S. B. (2013) · SPIE Proceedings · doi:10.1117/12.2002239 · Cited by 63 · see citing works
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NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Weng, Y., Zhou, T., Li, Y., & Qiu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908991 · Cited by 487 · see citing works
“[15] H. Lu et al. (2018).”
MethodSelf-citationOptimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search
Meng, L., Nie, F., Zhang, Y., & Han, C. (2026) · arXiv · doi:10.48550/arxiv.2602.22361
- Authors
- Lu, H., Li, Y., Uemura, T., Kim, H., & Serikawa, S.
- Affiliations
- Kyushu Institute of Technology (JP)
- Japan Society for the Promotion of Science (JP)
- Corresponding author
- Lu, Huimin
- ORCID
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 82
- Pages
- 142-148
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2782796222
- Cited by
- 290
- References
- 2
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 17 · 2023: 24 · 2022: 19 · 2021: 17 · 2020: 55 · 2019: 106 · 2018: 33
- FWCI
- 26.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Future Generation Computer Systems (publishedVersion)
- Funders
- Fundamental Research Developing Association for Shipbuilding and Offshore
- Japan Society for the Promotion of Science (17K14694, P18125)
- Ministry of Education, Culture, Sports, Science and Technology (16809746)
- Telecommunications Advancement Foundation
- State Key Laboratory of Ocean Engineering (1510)
- State Key Laboratory of Marine Geology (MGK1608)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Keywords
- light field imaging, low-light conditions, turbidity, light scattering, depth estimation, color correction, spectral properties, deep convolutional neural networks
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 ›
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