Abstract
Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the previous defogging approaches are considerably low since they fail to consider the image-specific cues. In this paper, a novel and simple defogging approach is proposed based on the estimation of depth map by considering the density of fog in local image regions. The proposed approach uses the scene-specific depth map information to compute the dark channel and transmission. The quality of recovered image is further improved by a post-processing technique. Experimental evaluation performed on FRIDA and FRIDA2 benchmark datasets demonstrates the proposed defogging framework outperforms state-of-the-art approaches. The code and the results of this work are open-sourced for reproducibility (https://github.com/RPRO5/Defogging).
Highlights
Poor visibility in outdoor images is one of the key challenge in many image understanding and computer vision-based applications such as traffic monitoring[1], automated driving[2], object detection[3], video surveillance[4], object tracking[5, 6] and aerial imagery[7]
Robust transmission estimation is important in all defogging frameworks since it is used to recover the fog-free image through equation 8
An image processing based simple but efficient framework is proposed for single image defogging
Summary
Poor visibility in outdoor images is one of the key challenge in many image understanding and computer vision-based applications such as traffic monitoring[1], automated driving[2], object detection[3], video surveillance[4], object tracking[5, 6] and aerial imagery[7]. Based on the observations of a large number of foggy and fog-free images, the ‘dark channel prior’[15] framework has been proposed and showed state-of-the-art results. This method lies in the observation of intensities of a few pixels are close to zero in at least one colour channel in a local patch of a fog-free image. A robust single image fog removal framework should have state-of-the-art recovering performance and computationally efficient since these are the requirements of many real-world applications To achieve this objective, we propose a novel defogging framework based on simple but efficient image enhancement techniques.
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