DRCDN: learning deep residual convolutional dehazing networks
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Since 2012, convolutional neural networks have been successfully applied to multimodal applications such as image dehazing and human activity recognition.
“Since its introduction by Krizhevsky et al. [7] in 2012, the CNN model has been successfully applied to a wide range of multimodal applications, such as image dehazing [239, 240] and human activity recognition [241].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [240]
The citation lists haven’t been fetched yet
References 64 · Cited by 158
- Authors
- Zhang, S., & He, F.
- Affiliations
- Wuhan University (CN)
- Corresponding author
- He, Fazhi
- Venue
- The Visual Computer
- Published
- 2019-11-29
- Volume
- 36
- Issue
- 9
- Pages
- 1797-1808
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W2990451522
- Cited by
- 158
- References
- 64
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 4 · 2023: 25 · 2022: 44 · 2021: 44 · 2020: 24
- FWCI
- 12.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Funders
- National Natural Science Foundation of China (61472289)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- single image dehazing, image dehazing, context aggregation, image refinement, end-to-end learning, ill-posed inverse problems
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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