ut ut

Laboratory for Image & Video Engineering

Perceptual Fog Density Assessment and Image Defogging Research at LIVE

The Perceptual Fog Density Assessment and Image Defogging Research at LIVE are being conducted in collaboration with Hongik Univ.


LIVE Image Defogging Database Release

     Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging NEW!

  • No-reference perceptual fog density prediction model based on natural scene statistics and fog aware statistical features.
  • No-reference perceptual image defogging algorithm.

Software Release

      Please cite the following papers in any published work if you use above database or software.
     
  • L. K. Choi, J. You, and A. C. Bovik, "Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888-3901, Nov. 2015. (PDF)
  • L. K. Choi, J. You, and A. C. Bovik, "Referenceless perceptual image defogging," IEEE Southwest Symposium on Image Analysis and Interpretation, Apr. 2014. (PDF)
  • L. K. Choi, J. You, and A. C. Bovik, "Referenceless perceptual fog density prediction model," SPIE Human Vision and Electronic Imaging, Feb. 2014. (PDF)
  • L. K. Choi, J. You, and A. C. Bovik, "LIVE Image Defogging Database," Online: http://live.ece.utexas.edu/research/fog/fade_defade.html, 2015.