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)
More Information
  • 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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  • [1]
    ORTEGA S, HALICEK M, FABELO H, et al. Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review[J]. Biomedical Optics Express, 2020, 11(6): 3195-3233. doi: 10.1364/BOE.386338
    王成, 刘宾, 周楚, 等. 窄带LED照明的多光谱显微成像系统研究[J]. 中国激光,2020,47(12):1207006. doi: 10.3788/CJL202047.1207006

    WANG CH, LIU B, ZHOU CH, et al. Multispectral microimaging system with narrowband LED illumination[J]. Chinese Journal of Lasers, 2020, 47(12): 1207006. (in Chinese) doi: 10.3788/CJL202047.1207006
    唐凌宇, 葛明锋, 董文飞. 全自动推扫式高光谱显微成像系统设计与研究[J]. 中国光学,2021,14(6):1486-1494. doi: 10.37188/CO.2021-0040

    TANG L Y, GE M F, DONG W F. Design and research of fully automatic push-broom hyperspectral microscopic imaging system[J]. Chinese Optics, 2021, 14(6): 1486-1494. (in Chinese) doi: 10.37188/CO.2021-0040
    SU W H, SUN D W. Multispectral imaging for plant food quality analysis and visualization[J]. Comprehensive Reviews in Food Science and Food Safety, 2018, 17(1): 220-239. doi: 10.1111/1541-4337.12317
    CHAMBINO L L, SILVA J S, BERNARDINO A. Multispectral facial recognition: a review[J]. IEEE Access, 2020, 8: 207871-207883. doi: 10.1109/ACCESS.2020.3037451
    WU F, JING X Y, FENG Y J, et al. Spectrum-aware discriminative deep feature learning for multi-spectral face recognition[J]. Pattern Recognition, 2021, 111: 107632. doi: 10.1016/j.patcog.2020.107632
    李云辉. 压缩光谱成像系统中物理实现架构研究综述[J]. 中国光学(中英文),2022,15(5):929-945. doi: 10.37188/CO.2022-0104

    LI Y H. Review of physical implementation architecture in compressive spectral imaging system[J]. Chinese Optics, 2022, 15(5): 929-945. (in Chinese) doi: 10.37188/CO.2022-0104
    杨鹰, 孔玲君, 刘真. 基于压缩感知的多光谱图像去马赛克算法[J]. 液晶与显示,2017,32(1):56-61. doi: 10.3788/YJYXS20173201.0056

    YANG Y, KONG L J, LIU ZH. Multi-spectral demosaicking method based on compressive sensing[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(1): 56-61. (in Chinese) doi: 10.3788/YJYXS20173201.0056
    HABTEGEBRIAL T A, REIS G, STRICKER D. Deep convolutional networks for snapshot hypercpectral demosaicking[C]. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, 2019: 1-5.
    FENG K, ZHAO Y Q, CHAN J C W, et al. Mosaic convolution-attention network for demosaicing multispectral filter array images[J]. IEEE Transactions on Computational Imaging, 2021, 7: 864-878. doi: 10.1109/TCI.2021.3102052
    肖树林, 胡长虹, 高路尧, 等. 像元映射变分辨率光谱成像重构[J]. 中国光学(中英文),2022,15(5):1045-1054.

    XIAO SH L, HU CH H, GAO L Y, et al. Pixel mapping variable-resolution spectral imaging reconstruction[J]. Chinese Optics, 2022, 15(5): 1045-1054. (in Chinese)
    MIAO L D, QI H R. The design and evaluation of a generic method for generating mosaicked multispectral filter arrays[J]. IEEE Transactions on Image Processing, 2006, 15(9): 2780-2791. doi: 10.1109/TIP.2006.877315
    MIAO L D, QI H R, RAMANATH R, et al. Binary tree-based generic demosaicking algorithm for multispectral filter arrays[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3550-3558. doi: 10.1109/TIP.2006.877476
    GUPTA M, RAM M. Weighted bilinear interpolation based generic multispectral image demosaicking method[J]. Journal of Graphic Era University, 2019, 7(2): 108-118.
    GUPTA M, RATHI V, GOYAL P. Adaptive and progressive multispectral image demosaicking[J]. IEEE Transactions on Computational Imaging, 2022, 8: 69-80. doi: 10.1109/TCI.2022.3140554
    孙帮勇, 袁年曾, 胡炳樑. 一种八谱段滤光片成像系统设计[J]. 光子学报,2020,49(5):0511001. doi: 10.3788/gzxb20204905.0511001

    SUN B Y, YUAN N Z, HU B L. Design of an eight-band filter imaging system[J]. Acta Photonica Sinica, 2020, 49(5): 0511001. (in Chinese) doi: 10.3788/gzxb20204905.0511001
    RATHI V, GOYAL P. Generic multispectral Demosaicking based on directional interpolation[J]. IEEE Access, 2022, 10: 64715-64728. doi: 10.1109/ACCESS.2022.3182493
    MONNO Y, TANAKA M, OKUTOMI M. Multispectral demosaicking using guided filter[J]. Proceedings of SPIE, 2012, 8299: 82990O. doi: 10.1117/12.906168
    任杰, 刘家瑛, 白蔚, 等. 基于隐式分段自回归模型的图像插值算法[J]. 软件学报,2012,23(5):1248-1259. doi: 10.3724/SP.J.1001.2012.04049

    REN J, LIU J Y, BAI W, et al. Image interpolation algorithm based on implicit piecewise autoregressive model[J]. Journal of Software, 2012, 23(5): 1248-1259. (in Chinese) doi: 10.3724/SP.J.1001.2012.04049
    ZHANG X J, WU X L. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation[J]. IEEE Transactions on Image Processing, 2008, 17(6): 887-896. doi: 10.1109/TIP.2008.924279
    HE K M, SUN J, TANG X O. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409. doi: 10.1109/TPAMI.2012.213
    LI ZH G, ZHENG J H, ZHU Z J, et al. Weighted guided image filtering[J]. IEEE Transactions on Image Processing, 2015, 24(1): 120-129. doi: 10.1109/TIP.2014.2371234
    XU L, YAN Q, XIA Y, et al. Structure extraction from texture via relative total variation[J]. ACM Transactions on Graphics, 2012, 31(6): 139.
    路陆, 姜鑫, 杨锦程, 等. 基于自适应引导滤波的红外图像细节增强[J]. 液晶与显示,2022,37(9):1182-1189. doi: 10.37188/CJLCD.2022-0024

    LU L, JIANG X, YANG J CH, et al. Adaptive guided filtering based infrared image detail enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(9): 1182-1189. (in Chinese) doi: 10.37188/CJLCD.2022-0024
    YASUMA F, MITSUNAGA T, ISO D, et al. Generalized assorted pixel camera: Postcapture control of resolution, dynamic range, and spectrum[J]. IEEE Transactions on Image Processing, 2010, 19(9): 2241-2253. doi: 10.1109/TIP.2010.2046811
    MONNO Y, KIKUCHI S, TANAKA M, et al. A practical one-shot multispectral imaging system using a single image sensor[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3048-3059. doi: 10.1109/TIP.2015.2436342
    PARK J I, LEE M H, GROSSBERG M D, et al. . Multispectral imaging using multiplexed illumination[C]. 2007 IEEE 11th International Conference on Computer Vision, IEEE, 2007: 1-8.
    SARA U, AKTER M, UDDIN M S. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study[J]. Journal of Computer and Communications, 2019, 7(3): 8-18. doi: 10.4236/jcc.2019.73002
    SHARMA G, WU W CH, DALAL E N. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations[J]. Color Research &Application, 2005, 30(1): 21-30.
    贾停停, 王慧琴, 王可, 等. 相位相关性增强的自适应低重叠率多光谱图像快速拼接算法[J]. 液晶与显示,2022,37(4):483-493. doi: 10.37188/CJLCD.2021-0294

    JIA T T, WANG H Q, WANG K, et al. Adaptive low overlap multispectral image fast mosaic algorithm based on phase correlation enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(4): 483-493. (in Chinese) doi: 10.37188/CJLCD.2021-0294
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