Single image haze removal based on haze physical characteristics and adaptive sky region detection
Outdoor images are often degraded by haze and other inclement weather conditions, which affect both consumer photographs and computer vision applications severely. Therefore, researchers have proposed plenty of restoration approaches to deal with this problem. However, it is hard to tackle the color distortion problem in restored images with ignoring the differences between fog and haze. Meanwhile, the atmospheric light is also an important variable that influences the global illumination of images. In this paper, we analyze the physical meaning of atmospheric light first, and estimate atmospheric light by a novel method of obtaining the sky region in images, which is based on our newly proposed sky region prior. Then after exploring physical characteristics of fog and haze, we explain why images taken in haze appear yellowish, and eliminate this phenomenon by our adaptive channel equalization method. Quantitative comparisons with seven state of art algorithms on a variety of real world haze images demonstrate that our algorithm can remove haze effectively and keep color fidelity better. HighlightsWe propose a novel single image haze removal approach.Our approach is based on haze physical character and adaptive sky region detection.We analyze the haze physical character and meaning of atmospheric scattering model.We propose a sky region prior based on thousands of outdoor images.We propose a new adaptive sky region detection method to estimate atmospheric light.We eliminate distortion in hazy images by our adaptive channel equalization method.We convert to HSI color space to compensate over saturation phenomenon in RGB space.
Neurocomputing