Abstract
Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.
Highlights
Often, when outdoor images are acquired under poor weather conditions, such as haze and fog, the visibility of the captured scene is prone to significant degradation
To eliminate the block effect and the negative effect caused by scene segmentation errors, we propose a guided total variation model (GTV) to perform guided smoothing, which the original total variation (TV) model was not equipped with [19,20]
The redefined model significantly simplifies the estimation of transmission, because the scene luminance and scene transmission need to be estimated for only a limited number of scenes
Summary
Often, when outdoor images are acquired under poor weather conditions, such as haze and fog, the visibility of the captured scene is prone to significant degradation (see Fig. 1a). Because the median filter shows poor edge preserving performance, the method left small amounts of mist around depth changes in the dehazed image To solve this problem, Xiao [11] proposed a guided joint bilateral filter for haze removal. Gibson [15] presented the median dark channel prior method based on [13], which accelerates the haze removal process to some extent, because it requires no refinement of the transmission map. This method fails to achieve good visual results.
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