Abstract
In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.
Highlights
Natural images captured outdoors are often degraded by bad weather [1], which greatly reduces the quality of captured images
Haze removal or image dehazing is urgently needed in computer vision applications
First of all, removing fog from a hazy image can significantly improve the visibility in the image and greatly rectify the color shift caused by the air-light
Summary
Natural images captured outdoors are often degraded by bad weather [1], which greatly reduces the quality of captured images. The assumption that the surface shading and the medium transmission functions are locally statistically uncorrelated solved a constant albedo and the atmospheric-albedo ambiguity to generate haze-free images It cannot process heavily hazy images and grayscale images well. From left to right: the hazy input, the estimated transmission map by the proposed algorithm, and the dehazed image. Following these existing methods, we further develop a new dehazing method based on a sparse contextual representation.
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