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

has been cited by the following article:

• TITLE: Simulation of Hazy Image and Validation of Haze Removal Technique

  AUTHORS: Aditi Sarker, Morium Akter, Mohammad Shorif Uddin

  KEYWORDS: Haze Simulation, Validation, Perline Noise, Depth Map, Dark-Channel Prior

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.2, February 26, 2019

  ABSTRACT: Haze hampers the performance of vision systems. So, removal of haze appearance in a scene should be the first-priority for clear vision. It finds wide spectrum of practical applications. A good number of dehazing techniques have already been developed. However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity. To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique. The simulated hazy image is developed using atmospheric model with and without Perlin noise. The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.