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De-Hazing using Guided and L0 Gradient Minimization Filters


Affiliations
1 School of Electronics Engineering, VIT University, Vellore, India
 

Objectives: Images captured under foggy or hazy conditions have low contrast and meager color. The proposed work is carried to improve the visibility of hazy images. Methods: The proposed method is based on ‘dark channel prior’ to remove haze, along with guided filter to refine the image due to the occurrence of halos. Even after refinement, halos are not completely removed. So, for further refinement edge preserving smoothing global filter L0 gradient minimization filter is used. Findings: This method has an advantage over conventional method by suppressing halos completely. Application/Improvements: The proposed method improves the contrast and increase in entropy reveals that the detail information is preserved effectively.

Keywords

Air-light, Attenuation, Dark Channel Prior, Guided Filter, L0gradient Minimization Filter.
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  • De-Hazing using Guided and L0 Gradient Minimization Filters

Abstract Views: 160  |  PDF Views: 0

Authors

T.N. Padmini
School of Electronics Engineering, VIT University, Vellore, India
T. Shankar
School of Electronics Engineering, VIT University, Vellore, India

Abstract


Objectives: Images captured under foggy or hazy conditions have low contrast and meager color. The proposed work is carried to improve the visibility of hazy images. Methods: The proposed method is based on ‘dark channel prior’ to remove haze, along with guided filter to refine the image due to the occurrence of halos. Even after refinement, halos are not completely removed. So, for further refinement edge preserving smoothing global filter L0 gradient minimization filter is used. Findings: This method has an advantage over conventional method by suppressing halos completely. Application/Improvements: The proposed method improves the contrast and increase in entropy reveals that the detail information is preserved effectively.

Keywords


Air-light, Attenuation, Dark Channel Prior, Guided Filter, L0gradient Minimization Filter.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i37%2F126655