Real-time image and video dehazing based on multiscale guided filtering

We propose a real-time dehazing algorithm for hazy images and videos based on multiscale guided filtering. The most time-consuming step in physical model-based algorithms is estimating the transmission map and atmospheric light. In this work, we develop a computationally efficient approach for the estimation. First, we construct an image pyramid from a hazy image. Then, we estimate the transmission map and atmospheric light at the coarsest level. Next, we obtain the transmission at the finest level by iterative upsampling with guide image filtering to avoid information loss. Furthermore, we extend the single-image dehazing algorithm to real-time video dehazing to reduce flickering artifacts in dehazed videos by making transmission values temporally coherent. Experimental results show that the proposed algorithm is applicable in real-time applications, while providing comparable or even better performance than that of state-of-the-art algorithms.