Single image dehazing based on bright channel prior model and saliency analysis strategy
Libao Zhang, Shan Wang, Xiaohan Wang · IET Image Processing · 2020
Abstract Haze is a common atmospheric phenomenon that causes poor visibility in outdoor images, which greatly limits image application in later stages. Therefore, haze removal has become the first and most indispensable step when dealing with degraded images. In this paper, we propose a novel bright channel prior (BCP) model and a saliency analysis strategy for haze removal. First, we obtain a more robust and accurate atmospheric light by a superpixel‐based dark channel method. Second, we utilize the dark channel prior (DCP) to handle dark regions in hazy images. However, the DCP often mistakes white regions for opaque haze and thus causes serious colour distortion and halo effects. To solve this problem, a new BCP is proposed to accurately estimate the transmission of bright regions in hazy images. Third, we fuse the DCP and BCP using a multiscale fusion strategy with Laplacian pyramid representation to gain the correct transmission information for both bright and dark regions. Finally, a novel saliency analysis strategy for transmission refinement is proposed, so that the texture details can remain present to the greatest extent in the restored images. The experimental results illustrate that our proposed method performs well in restoring images containing bright objects.