Fog Removal Algorithm for Geographic Images Using Generative Adversarial Nets

Abstract

In order to improve the imaging effect of geographic images in a complex weather environment. This paper designs a geographic image defogging algorithm by using the generation countermeasure network to improve the clarity of the image. Firstly, the imaging mode and degradation process of the foggy image is studied in this paper, and the atmospheric scattering model is established to simulate the degradation of the image. Secondly, according to the data characteristics of image defogging, an optimized image defogging model is generated based on Conditional Generation Adversarial Nets (CGAN), so that the image output by the model is closer to the real image. The experimental results show that the image defogging model designed in this paper has good contrast and color richness, which can effectively solve the problem of image degradation in fog. Moreover, the reduction of paired data sets will reduce the measurement index of the geographic image defogging model. This paper provides a reference for the study of geographic image defogging.