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Single image dehazing
Abstract
In this paper we present a new method for estimating the optical transmission in hazy scenes given a single input image. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scene contrasts. In this new approach we formulate a refined image formation model that accounts for surface shading in addition to the transmission function. This allows us to resolve ambiguities in the data by searching for a solution in which the resulting shading and transmission functions are locally statistically uncorrelated. A similar principle is used to estimate the color of the haze. Results demonstrate the new method abilities to remove the haze layer as well as provide a reliable transmission estimate which can be used for additional applications such as image refocusing and novel view synthesis.
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Citations
Cited by
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
Color balance and fusion for underwater image enhancement
Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2759252 · Cited by 1,357
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
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
“,37]. Most of the early single image dehazing methods solve the optical model of Koschmieder [26] by searching for different priors that capture statistical properties of the hazy and non-hazy images [20,38,24,40,27,10,1,21,19,39]. One of the major obstacles in image dehazing is the validation of the proposed methods.…”
BackgroundDeepTAM: Deep Tracking and Mapping
Zhou, H., Ummenhofer, B., & Brox, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_50 · Cited by 226
Metadata
- Authors
- Fattal, R.
- Venue
- ACM SIGGRAPH 2008 papers
- Publisher
- ACM
- Published
- 2008-08
- Pages
- 1-9
- OpenAlex
- W4252054347
- Cited by
- 374
- References
- 0
- FWCI
- 16.29
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fattal, R. (2008). Single image dehazing. ACM SIGGRAPH 2008 papers. https://doi.org/10.1145/1399504.1360671
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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.
