Image Defogging Algorithm Based on Dark Channel Prior and Particle Swarm Optimization
In the case of haze, aiming at the shortcomings of traditional fixed-value dark channel prior algorithm,such as low image quality and color distortion,a defogging improve ment algorithm integrating dark channel prior and particle swarm optimization is proposed.According to the characteristics of the particle swarm optimization algorithm, the best value of the retention factor in each average brightness range is optimized and brought into the dark channel prior algorithm.At the same time, the median filtering algorithm is used to replace the original two minimum filtering algorithms when solving the atmospheric light value.The experimental results show that the proposed algorithm has better subjective visual effects and objective evaluation criteria in image defogging compared with the traditional fixed value dark channel prior algorithm,and the operation speed of the algorithm is improved by about 14.3％.
NETL
NSTL
万方数据
