Color Correction and Repair of Haze Images Under Hue-saturation-intensity Color Space and Machine Learning

Abstract

To improve the visual effect and quality of haze images after fog removal, a model for color correction and repair of haze images under hue-saturation-intensity (HSI) color space combined with machine learning is proposed. First, the haze image imaging model is constructed according to the atmospheric scattering theory. Second, based on HSI color space, the color enhancement and fog removal of the haze image model is proposed, and a haze image-transmittancegallery is constructed. Third, the visual dictionary of the transmittance graph is obtained by training the k-means clustering algorithm based on density parameter optimization and support vector machine algorithm based on genetic algorithm optimization. Fourth, based on the visual dictionary and the atmospheric scattering model, the haze image is repaired and defogged, and the subjective visual effects and objective evaluation indexes of color enhancement and fog removal of haze images are compared. It is concluded that the algorithm can effectively guarantee the detail and clarity of the image after defogging.