Volume 16 Issue 5
Sep.  2023
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QI Hai-chao, SONG Yan-song, ZHANG Bo, LIANG Zong-lin, YAN Gang-qi, XUE Jia-yin, ZHANG Yi-qun, REN Bin. Multispectral demosaicing method based on an improved guided filter[J]. Chinese Optics, 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231
Citation: QI Hai-chao, SONG Yan-song, ZHANG Bo, LIANG Zong-lin, YAN Gang-qi, XUE Jia-yin, ZHANG Yi-qun, REN Bin. Multispectral demosaicing method based on an improved guided filter[J]. Chinese Optics, 2023, 16(5): 1056-1065. doi: 10.37188/CO.2022-0231

Multispectral demosaicing method based on an improved guided filter

doi: 10.37188/CO.2022-0231
Funds:  Supported by National Key R & D Program of China (No. 2022YFB3902500); National Natural Science Foundation of China (No.U2141231); the Natural Science Foundation of Jilin Province (No. 202002036JC); The Major Key Project of PCL (No. PCL2021A03-1)
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  • Corresponding author: songyansong2006@126.com
  • Received Date: 13 Nov 2022
  • Rev Recd Date: 12 Dec 2022
  • Available Online: 17 Apr 2023
  • In order to better preserve high-frequency information in demosaicing multispectral images, we propose a new demosaicing method for multispectral images based on an improved guided filter. Firstly, the strong correlation between adjacent pixels based on the autoregressive model is constructed, gradually estimates the model parameters at each pixel, and the optimal estimation value is obtained by minimizing the estimation error in the local window, interpolates the sampling dense band G, and generates high-quality guide images. The windowed intrinsic variation coefficient is then introduced into the penalty factor to obtain a weighted guide filter with edge sensing ability and to reconstruct the remaining sparse sampling bands. Finally, the CAVE dataset and the TokyoTech dataset are used for simulation. The experimental results show that compared with the mainstream five-band multispectral image demosaicing method, the peak signal-to-noise ratio and structure similarity of the reconstructed image in the CAVE dataset and the TokyoTech dataset are improved by 3.40%, 2.02%, 1.34%, 0.30% and 6.11%, 5.95%, 2.28%, 1.42%, respectively. The local structure and color information of the original image are also better preserved, and the edge artifacts and noise are reduced.

     

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