Relative CNN-RNN: Learning relative atmospheric visibility from images

Published in IEEE Transactions on Image Processing, 2018

Yang You, Cewu Lu, Weiming Wang, Chi-Keung Tang

PDF / BibTeX

Abstract

We propose a deep learning approach for directly estimating relative atmospheric visibility from outdoor photos without relying on weather images or data that require expensive sensing or custom capture. Our data-driven approach capitalizes on a large collection of Internet images to learn rich scene and visibility varieties. The relative CNN-RNN coarse-to-fine model, where CNN stands for convolutional neural network and RNN stands for recurrent neural network, exploits the joint power of relative support vector machine, which has a good ranking representation, and the data-driven deep learning features derived from our novel CNN-RNN model.

BibTeX

@article{you2018relative,
  title     = {Relative CNN-RNN: Learning relative atmospheric visibility from images},
  author    = {Yang You and Cewu Lu and Weiming Wang and Chi-Keung Tang},
  journal   = {IEEE Transactions on Image Processing (TIP)},
  year      = {2018}
}

Recommended citation: You, Y., Lu, C., Wang, W., & Tang, C. K. (2018). Relative CNN-RNN: Learning relative atmospheric visibility from images. IEEE Transactions on Image Processing, 28(1), 45-55.