Single Image Haze Removal Using Dark Channel Prior. He, K., Sun, J., & Tang, X. In pages 1956–1963, June, 2009. IEEE Computer Society. ISSN: 1063-6919![Single Image Haze Removal Using Dark Channel Prior [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.
@inproceedings{he_single_2009,
title = {Single {Image} {Haze} {Removal} {Using} {Dark} {Channel} {Prior}},
isbn = {978-1-4244-3992-8},
url = {https://www.computer.org/csdl/proceedings-article/cvpr/2009/05206515/12OmNvA1hCU},
doi = {10.1109/CVPR.2009.5206515},
abstract = {In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal.},
language = {en},
urldate = {2023-08-13},
publisher = {IEEE Computer Society},
author = {He, Kaiming and Sun, Jian and Tang, Xiaoou},
month = jun,
year = {2009},
note = {ISSN: 1063-6919},
keywords = {\#CVPR{\textgreater}09, \#Machine Learning, \#Vision, /unread},
pages = {1956--1963},
}
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