The DOI matches its Crossref record.
BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES
Open accessAbstract
The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, differentmethods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators.
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
Cited by
An Underwater Image Enhancement Benchmark Dataset and Beyond
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2955241 · Cited by 2,373
Fast visibility restoration from a single color or gray level image
Tarel, J.-P., & Hautiere, N. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459251 · Cited by 1,313
Self-citationAutomatic Red-Channel underwater image restoration
Galdran, A., Pardo, D., Picón, A., & Alvarez-Gila, A. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.11.006 · Cited by 995
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
Ancuti, C. O., Ancuti, C., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00230 · Cited by 517
Zero-Shot Restoration of Underexposed Images via Robust Retinex Decomposition
Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., & Zhou, Y. (2020) · 2020 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102962 · Cited by 317
Metadata
- Authors
- Hautière, N., Tarel, J.-P., Aubert, D., & Dumont, É.
- Venue
- Image Analysis & Stereology
- Published
- 2011-05-03
- Volume
- 27
- Issue
- 2
- Pages
- 87
- ISSN
- 1580-3139 (ISSN-L)
- 1854-5165
- OpenAlex
- W1979666307
- Cited by
- 742
- References
- 0
- FWCI
- 20.01
- Type
- journal article
- Language
- en
- Open access
- gold
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: BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES. https://app.cochat.ai/papers/10.5566/ias.v27.p87-95
- The paper
- Hautière, N., Tarel, J.-P., Aubert, D., & Dumont, É. (2011). BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES. Image Analysis & Stereology. https://doi.org/10.5566/ias.v27.p87-95
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.
