Image Defogging Algorithm Based on Sparse Representation

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightn...

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Main Authors: Di Fan, Xinyun Guo, Xiao Lu, Xiaoxin Liu, Bo Sun
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/6835367
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author Di Fan
Xinyun Guo
Xiao Lu
Xiaoxin Liu
Bo Sun
author_facet Di Fan
Xinyun Guo
Xiao Lu
Xiaoxin Liu
Bo Sun
author_sort Di Fan
collection DOAJ
description Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.
format Article
id doaj-art-019f88798dda44668041bdc6946ccd65
institution DOAJ
issn 1076-2787
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language English
publishDate 2020-01-01
publisher Wiley
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series Complexity
spelling doaj-art-019f88798dda44668041bdc6946ccd652025-08-20T03:22:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/68353676835367Image Defogging Algorithm Based on Sparse RepresentationDi Fan0Xinyun Guo1Xiao Lu2Xiaoxin Liu3Bo Sun4College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, ChinaCollege of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, ChinaCollege of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, ChinaCollege of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, ChinaCollege of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271000, Shandong, ChinaAiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.http://dx.doi.org/10.1155/2020/6835367
spellingShingle Di Fan
Xinyun Guo
Xiao Lu
Xiaoxin Liu
Bo Sun
Image Defogging Algorithm Based on Sparse Representation
Complexity
title Image Defogging Algorithm Based on Sparse Representation
title_full Image Defogging Algorithm Based on Sparse Representation
title_fullStr Image Defogging Algorithm Based on Sparse Representation
title_full_unstemmed Image Defogging Algorithm Based on Sparse Representation
title_short Image Defogging Algorithm Based on Sparse Representation
title_sort image defogging algorithm based on sparse representation
url http://dx.doi.org/10.1155/2020/6835367
work_keys_str_mv AT difan imagedefoggingalgorithmbasedonsparserepresentation
AT xinyunguo imagedefoggingalgorithmbasedonsparserepresentation
AT xiaolu imagedefoggingalgorithmbasedonsparserepresentation
AT xiaoxinliu imagedefoggingalgorithmbasedonsparserepresentation
AT bosun imagedefoggingalgorithmbasedonsparserepresentation