Tang, K.T., Yang, J.C. and Wang, J. (2014) Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, 23-28 June 2014, 2995-3002.
http://dx.doi.org/10.1109/cvpr.2014.383

has been cited by the following article:

• TITLE: A Research on Single Image Dehazing Algorithms Based on Dark Channel Prior

  AUTHORS: Ebtesam Mohameed Alharbi, Peng Ge, Hong Wang

  KEYWORDS: Image Dehazing, Dark Channel

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.2, February 25, 2016

  ABSTRACT: In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper studies the state-of-the-art in this area and puts forwards their strengths and weaknesses. Through experiments the efficiencies and shortcomings of these algorithms are shared. This information is essential for researchers and developers in providing a reference for the development of applications and future of the research field.