Learning based single image dehazing via genetic programming
A genetic programming (GP) based framework to learn the effective feature representation for image dehazing 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.
2016 23rd International Conference on Pattern Recognition (ICPR)