Single Image Dehazing with V-transform and Dark Channel Prior

Wang Xiaochun (), Sun Xiangdong () and Song Ruixia ()
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Wang Xiaochun: College of Sciences, Beijing Forestry University, Beijing100083, China
Sun Xiangdong: College of Sciences, North China University of Technology, Beijing100144, China
Song Ruixia: College of Sciences, North China University of Technology, Beijing100144, China

Journal of Systems Science and Information, 2020, vol. 8, issue 2, 185-194

Abstract: Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion. To improve these limitations, this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior, in which a hazy RGB image is converted into the HSI color space, and each component H, I and S is processed separately. The hue component H remains unchanged, the saturation component S is stretched after being denoised by a median filter. In the procession of intensity component, a quad-tree algorithm is presented to estimate the atmospheric light, the dark channel prior and the V-transform are used to estimate the transmission map. To reduce the computational complexity, the intensity component I is decomposed by the V-transform first, coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image, and the guided filter is finally employed to refine the coarse transmission map. For images with sky regions, the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map. The proposed algorithm has low time complexity and performs well on a wide variety of images. The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.

Keywords: dark channel prior; image haze removal; HSI color space; quad-tree; V-transform (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:8:y:2020:i:2:p:185-194:n:7

DOI: 10.21078/JSSI-2020-185-10

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