The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Open accessAbstract
Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“[46] replace the time-consuming soft matting [44] with standard median filtering, “median of median filter”, guided joint bilateral filtering [37]–[42] and guided image filtering, respectively.”
MethodHe, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
High-accuracy stereo depth maps using structured light
Scharstein, D., & Szeliski, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211354 · Cited by 1,542
Cited by
DehazeNet: An End-to-End System for Single Image Haze Removal
Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367
Fast Image Dehazing Method Based on Linear Transformation
Wang, W., Yuan, X., Wu, X., & Liu, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2652069 · Cited by 319
FAMED-Net: A Fast and Accurate Multi-Scale End-to-End Dehazing Network
Zhang, J., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2922837 · Cited by 196
Dehazing Evaluation: Real-World Benchmark Datasets, Criteria, and Baselines
Zhao, S., Zhang, L., Huang, S., Shen, Y., & Zhao, S. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2995264 · Cited by 169
DRCDN: learning deep residual convolutional dehazing networks
Zhang, S., & He, F. (2020) · The Visual Computer · doi:10.1007/s00371-019-01774-8 · Cited by 158
Metadata
- Authors
- Zhu, Q., Mai, J., & Shao, L.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2015-11
- Volume
- 24
- Issue
- 11
- Pages
- 3522-3533
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2156936307
- Cited by
- 2,469
- References
- 5
- FWCI
- 90.98
- Type
- journal article
- Language
- en
- Open access
- green
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