Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion
Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disord...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2023/8891239 |
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| _version_ | 1832559558480887808 |
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| author | Shaojin Ma Weiguo Pan Hongzhe Liu Songyin Dai Bingxin Xu Cheng Xu Xuewei Li Huaiguang Guan |
| author_facet | Shaojin Ma Weiguo Pan Hongzhe Liu Songyin Dai Bingxin Xu Cheng Xu Xuewei Li Huaiguang Guan |
| author_sort | Shaojin Ma |
| collection | DOAJ |
| description | Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects. |
| format | Article |
| id | doaj-art-64cd4bb3205f42dfa523b6122beb2e1a |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-64cd4bb3205f42dfa523b6122beb2e1a2025-02-03T01:29:48ZengWileyAdvances in Multimedia1687-56992023-01-01202310.1155/2023/8891239Image Dehazing Based on Improved Color Channel Transfer and Multiexposure FusionShaojin Ma0Weiguo Pan1Hongzhe Liu2Songyin Dai3Bingxin Xu4Cheng Xu5Xuewei Li6Huaiguang Guan7Beijing Key Laboratory of Information Service EngineeringBeijing Key Laboratory of Information Service EngineeringBeijing Key Laboratory of Information Service EngineeringBeijing Key Laboratory of Information Service EngineeringBeijing Key Laboratory of Information Service EngineeringBeijing Key Laboratory of Information Service EngineeringBeijing Jiaotong UniversityCATARC (Tianjin) Automotive Engineering Research Institute Co., Ltd.Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects.http://dx.doi.org/10.1155/2023/8891239 |
| spellingShingle | Shaojin Ma Weiguo Pan Hongzhe Liu Songyin Dai Bingxin Xu Cheng Xu Xuewei Li Huaiguang Guan Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion Advances in Multimedia |
| title | Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion |
| title_full | Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion |
| title_fullStr | Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion |
| title_full_unstemmed | Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion |
| title_short | Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion |
| title_sort | image dehazing based on improved color channel transfer and multiexposure fusion |
| url | http://dx.doi.org/10.1155/2023/8891239 |
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