Enhancement of Ground-based Cloud Image by Genetic Algorithm with SURF Points

摘要

The ground-based cloud image is a valuable data source for meteorological observation. For the purpose of cloud type recognition and cloud height measurement, it is essential to enhance the ground-based cloud image. However, exiting image enhancement approach may not achieve optimal performance in this application. In this paper, we propose a genetic algorithm based method to enhance the ground-based cloud image. Inspired by the interest points matching technique of stereo, we propose a new criterion that use SURF interest points to construct fitness function of the genetic algorithm. In addition, in order to increase the diversity of the population, we propose a novel initialization that contains two strategies: random numbers and specified numbers according to the regularized incomplete beta function. The experimental results show that our method is suitable for enhancing the ground-based cloud image compare with other existing methods.