Real-time image dehazing by superpixels segmentation and guidance filter

Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems.