To address false candidate atmospheric light and halo effects in dark channel prior-based image dehazing methods

an improved image dehazing algorithm is proposed

with atmospheric light validation and halo elimination strategies

which can reveal high visibility in haze-free images. As dark channel prior-based methods do not verify the validity of atmospheric light and may fail to select a proper one

a support vector machine-based classifier is trained and utilized to reject the false candidate atmospheric light. The halo effect is introduced to haze free results with coarse transmission maps in dark channel prior-based approaches. Thus

a patch shift-based fine transmission estimation method is adopted

which can preserve edges in the input image and suppress halo effects in the haze-free image significantly. Several pixels may still remain with halo effects near sharp edges

which are detected and corrected by the proposed halo elimination strategy using the guided filter. Finally

the haze-free image is obtained by solving the haze image formation model. Experimental results demonstrate that the false candidate atmospheric light is rejected and the halo effect is diminished significantly when our algorithm is applied. The resulting haze-free images possess superior visibility

rich image details

and depth. Our algorithm outperforms the state-of-the-art image dehazing methods and significantly improves visibility in haze-free images

which meets the requirements of applications such as video surveillance

traffic navigation

and object detection.