An Improved Haze Removal Algorithm Based on Genetic Fuzzy Clustering

Aiming at the degeneration phenomenon of images taken in mist, according to the features of the degraded images, an improved haze removal algorithm based on genetic fuzzy clustering is presented in this paper after analyzing its defects and shortcomings. Firstly, the improved atmospheric scattering model is established. Secondly, the original image is converted into a standard image through the improved model, and then we present a new multi-scale image edge detection by genetic fuzzy clustering, Based on this observation, we can use the multi-scale image edge detection to estimate the haze thickness directly and recover a high quality haze-free image. The new algorithm uses good global search ability of the genetic algorithm, which will implement the transfer from the scene defogging problem into the optimal estimation problem under global contrast optimal point. Compared with other algorithms for degraded images, the improved haze removal algorithm not only detects image edge precisely, but also has better performance in situations of dense haze. Theoretical analysis and experimental results demonstrate that, the new algorithm improved in this paper are effective for removal of fog-degraded images, and can be applied to the practical situations.