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.
|Title of host publication||Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|State||Published - 9 Dec 2016|
|Event||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States|
Duration: 26 Jun 2016 → 1 Jul 2016
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Period||26/06/16 → 1/07/16|
Bibliographical notePublisher Copyright:
© 2016 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition