Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model
Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/1/233 |
| _version_ | 1827667706186301440 |
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| author | Weijie Chen Zhenhong Jia Jie Yang Nikola K. Kasabov |
| author_facet | Weijie Chen Zhenhong Jia Jie Yang Nikola K. Kasabov |
| author_sort | Weijie Chen |
| collection | DOAJ |
| description | Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods. |
| first_indexed | 2024-03-10T03:23:00Z |
| format | Article |
| id | doaj.art-4bcb8557a60b46e999fca828c0a21d46 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:23:00Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4bcb8557a60b46e999fca828c0a21d462023-11-23T12:15:16ZengMDPI AGRemote Sensing2072-42922022-01-0114123310.3390/rs14010233Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential ModelWeijie Chen0Zhenhong Jia1Jie Yang2Nikola K. Kasabov3The Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaThe Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, ChinaKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New ZealandCompared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.https://www.mdpi.com/2072-4292/14/1/233multispectral image enhancementremote sensingdark channel priorfractional differential |
| spellingShingle | Weijie Chen Zhenhong Jia Jie Yang Nikola K. Kasabov Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model Remote Sensing multispectral image enhancement remote sensing dark channel prior fractional differential |
| title | Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model |
| title_full | Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model |
| title_fullStr | Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model |
| title_full_unstemmed | Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model |
| title_short | Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model |
| title_sort | multispectral image enhancement based on the dark channel prior and bilateral fractional differential model |
| topic | multispectral image enhancement remote sensing dark channel prior fractional differential |
| url | https://www.mdpi.com/2072-4292/14/1/233 |
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