Learning-based single image dehazing via genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/icpr/LeeS16,

• author = "Chulwoo Lee and Ling Shao",
• title = "Learning-based single image dehazing via genetic programming",
• 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, http://dblp.uni-trier.de/db/conf/icpr/icpr2016.html#LeeS16",
• 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

Citations