The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Fast Image Dehazing Method Based on Linear Transformation
Abstract
Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.
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
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
Single image haze removal using dark channel prior
He, K., Sun, J., & Tang, X. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.168 · Cited by 7,601
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Zhu, Q., Mai, J., & Shao, L. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2446191 · Cited by 2,469
Visibility in bad weather from a single image
Tan, R. T. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587643 · Cited by 2,280
Fattal, R. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360671 · Cited by 2,114
Contrast restoration of weather degraded images
Narasimhan, S. G., & Nayar, S. K. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1201821 · Cited by 1,642
Synthesis and evaluation of linear motion transitions
Wang, J., & Bodenheimer, B. (2008) · ACM Transactions on Graphics · doi:10.1145/1330511.1330512 · Cited by 1,534
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
Underwater Image Enhancement by Wavelength Compensation and Dehazing
Chiang, J. Y., & Chen, Y.-C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2179666 · Cited by 1,225
Metadata
- Authors
- Wang, W., Yuan, X., Wu, X., & Liu, Y.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2017-06
- Volume
- 19
- Issue
- 6
- Pages
- 1142-1155
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2576444478
- Cited by
- 319
- References
- 40
- FWCI
- 9.88
- Type
- journal article
- Language
- en
- Open access
- closed
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 Image Dehazing Method Based on Linear Transformation. https://app.cochat.ai/papers/10.1109/tmm.2017.2652069
- The paper
- Wang, W., Yuan, X., Wu, X., & Liu, Y. (2017). Fast Image Dehazing Method Based on Linear Transformation. IEEE Transactions on Multimedia. https://doi.org/10.1109/tmm.2017.2652069
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.
