The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Non-local Image Dehazing
Abstract
Haze limits visibility and reduces image contrast in outdoor images. The degradation is different for every pixel and depends on the distance of the scene point from the camera. This dependency is expressed in the transmission coefficients, that control the scene attenuation and amount of haze in every pixel. Previous methods solve the single image dehazing problem using various patch-based priors. We, on the other hand, propose an algorithm based on a new, non-local prior. The algorithm relies on the assumption that colors of a haze-free image are well approximated by a few hundred distinct colors, that form tight clusters in RGB space. Our key observation is that pixels in a given cluster are often non-local, i.e., they are spread over the entire image plane and are located at different distances from the camera. In the presence of haze these varying distances translate to different transmission coefficients. Therefore, each color cluster in the clear image becomes a line in RGB space, that we term a haze-line. Using these haze-lines, our algorithm recovers both the distance map and the haze-free image. The algorithm is linear in the size of the image, deterministic and requires no training. It performs well on a wide variety of images and is competitive with other stateof-the-art methods.
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Citations
Cites
Single image haze removal using dark channel prior
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Visibility in bad weather from a single image
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Fast visibility restoration from a single color or gray level image
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Fattal, R. (2014) · ACM Transactions on Graphics · doi:10.1145/2651362 · Cited by 1,167
Instant dehazing of images using polarization
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Single Image Dehazing by Multi-Scale Fusion
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Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing
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Single Image Dehazing Using Color Attenuation Prior
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Cited by
Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
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
“[13] solution is based on the color consistency observation introduced by Omer et al .”
BackgroundMethodInfluentialAll in One Bad Weather Removal Using Architectural Search
Li, R., Tan, R. T., & Cheong, L.-F. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00324 · Cited by 377
Fast Image Processing with Fully-Convolutional Networks
Chen, Q., Xu, J., & Koltun, V. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.273 · Cited by 341
Metadata
- Authors
- Berman, D., Treibitz, T., & Avidan, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1674-1682
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2467473805
- Cited by
- 1,642
- References
- 20
- FWCI
- 38.88
- Type
- conference paper
- Language
- en
- Open access
- closed
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