Learning-based single image dehazing via genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Chulwoo Lee and Ling Shao",
  title =        "Learning-based single image dehazing via genetic
  booktitle =    "23rd International Conference on Pattern Recognition
                 (ICPR 2016)",
  year =         "2016",
  pages =        "745--750",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-4847-2",
  bibdate =      "2017-05-24",
  bibsource =    "DBLP,
  URL =          "http://ieeexplore.ieee.org/document/7899724/",
  DOI =          "doi:10.1109/ICPR.2016.7899724",
  abstract =     "A genetic programming (GP)-based framework to learn
                 the effective feature representation for image
                 de-hazing is proposed in this work. In GP, an
                 individual program is randomly generated and
                 genetically evolved to achieve the desired goal. To
                 make GP estimate haze in an input image, a set of
                 operators and operands is designed, each of which is a
                 primitive of a GP program. Specifically, we provide
                 four basic features as candidates, and also include
                 function operators to construct sophisticated
                 representations of these features. After the entire GP
                 process finishes, we obtain a near-optimal compact
                 descriptor for haze estimation. Experimental results
                 demonstrate that the proposed algorithm enhances the
                 visual quality of haze-degraded images both objectively
                 and subjectively.",

Genetic Programming entries for Chulwoo Lee Ling Shao