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Article

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

1
The Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
3
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editor: Angel D. Sappa
Remote Sens. 2022, 14(1), 233; https://doi.org/10.3390/rs14010233
Received: 14 November 2021 / Revised: 31 December 2021 / Accepted: 3 January 2022 / Published: 5 January 2022
(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)
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. View Full-Text
Keywords: multispectral image enhancement; remote sensing; dark channel prior; fractional differential multispectral image enhancement; remote sensing; dark channel prior; fractional differential
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MDPI and ACS Style

Chen, W.; Jia, Z.; Yang, J.; Kasabov, N.K. Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model. Remote Sens. 2022, 14, 233. https://doi.org/10.3390/rs14010233

AMA Style

Chen W, Jia Z, Yang J, Kasabov NK. Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model. Remote Sensing. 2022; 14(1):233. https://doi.org/10.3390/rs14010233

Chicago/Turabian Style

Chen, Weijie, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. 2022. "Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model" Remote Sensing 14, no. 1: 233. https://doi.org/10.3390/rs14010233

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