Abstract
Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.
Highlights
In bad weather, small floating particles in the air seriously degrade image quality
To solve the above issues, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion
A single foggy image is first corrected by gamma correction, and a set of underexposed images is obtained
Summary
Small floating particles (such as dust, smoke, etc.) in the air seriously degrade image quality. Most of image restoration-based defogging methods rely on the responses of atmospheric degradation models These methods need to extract the a priori information of foggy images. The detailed information in foggy scenes can be improved by image enhancement These methods do not need to consider the physical causes (such as fog/haze) of image degradation, and can effectively avoid the a priori estimation of the scene depth and depth mapping process. To solve the above issues, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion.
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