Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.
|Title of host publication||2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 16 Jun 2017|
|Event||2017 IEEE International Conference on Computational Photography, ICCP 2017 - Stanford, United States|
Duration: 12 May 2017 → 14 May 2017
|Name||2017 IEEE International Conference on Computational Photography, ICCP 2017 - Proceedings|
|Conference||2017 IEEE International Conference on Computational Photography, ICCP 2017|
|Period||12/05/17 → 14/05/17|
Bibliographical noteFunding Information:
This work was supported by the The Leona M. and Harry B. Helmsley Charitable Trust and The Maurice Hatter Foundation, and the Technion Ollendorff Minerva Center for Vision and Image Sciences. Part of this research was supported by ISF grant 1917/2015. Dana Berman is partially supported by Apple Graduate Fellowship.
© 2017 IEEE.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Computational Theory and Mathematics
- Computer Vision and Pattern Recognition