Research on image de‐disturbing algorithm based on dark channel prior and anisotropic Gaussian filtering

Abstract

SummaryIn order to solve the problem of serious degradation of images collected outdoors in dense fog weather, a defogging algorithm for dense fog images was proposed. The fog‐day imaging physical model was simplified; the concept of fog concentration factor was proposed. The single image de‐hazing algorithm based on dark channel priors solves the problem of estimating the transmittance of fog and sky scenes. The image recovered by the algorithm is clear and natural. The algorithm has high computational complexity. It takes a long time to meet real‐time requirements. The principle of anisotropic Gaussian filtering is introduced. It combines with the dark channel. The value of the fog concentration coefficient is obtained by estimating the visibility value of a single fog image. It is then combined with an anisotropic Gaussian filter of the image to estimate the atmospheric light value. Defogging of the fog image is performed by using the repair function. It is necessary to perform effective de‐hazing processing on smog images. Experiments show that the improved algorithm can greatly reduce the complexity of the algorithm while ensuring the defogging effect of the original algorithm.