Image Defogging Algorithm Based on Grey World Theory and Dark Channel Priorities

Images collected outdoors are usually severely affected by adverse weather conditions, which causes great inconvenience to automatic data processing systems, such as haze. Haze leads to loss of image details, color distortion, and blurring of depth information, which creates a significant obstacle to analyzing targets for many smart devices on a daily basis, and thus image defogging is important in daily life. The widely used dark channel prior (DCP) based defogging methods have some deficiencies, so the traditional dark channel prior algorithms may not be effective in defogging. In order to improve the dark channel prior algorithm, this paper improves the dark channel prior algorithm based on the dark channel and introduces the gray world theory (GWT), and proposes a single-image defogging method using the improved gray world theory and the dark channel prior. Experimental results show that the algorithm in this paper can effectively reduce the problems of image over-darkness and image distortion.