Laser Technology, Volume. 48, Issue 4, 491(2024)

Adaptive deep prior for hyperspectral image super-resolution

MA Fei, WANG Fang*, and HUO Shuai
Author Affiliations
  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
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    References(22)

    [4] [4] YOKOYA N, YAIRI T, IWASAKI A. Coupled nonnegative matrix factorizationunmixing for hyperspectral and multispectral data fusion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 50(2): 528-537.

    [5] [5] YANG F X, PING Z L, MA F, et al. Fusion of hyperspectral and multispectral images with sparse and proximal regularization[J]. IEEE Access, 2019, 7: 186352-186363.

    [6] [6] AHMAD T, LYNGDOH R B, ANAND S S, et al. Robust coupled non-negative matrix factorization for hyperspectral and multispectral data fusion[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. New York, USA: IEEE Press, 2021: 2456-2459.

    [7] [7] KANATSOULIS C I, FU X, SIDIROPOULOS N D, et al. Hyperspectral super-resolution: A coupled tensor factorization approach[J]. IEEE Transactions on Signal Processing, 2018, 66(24): 6503-6517.

    [8] [8] MA F, HUO S, YANG F X. Graph-based logarithmic low-rank tensor decomposition for the fusion of remotely sensed images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11271-11286.

    [9] [9] HU J F, HUANG T Zh, DENG L J, et al. Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(12): 7251-7265.

    [10] [10] XIAO J J, LI J, YUAN Q Q, et al. Physics-based GAN with iterative refinement unit for hyperspectral and multispectral image fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6827-6841.

    [11] [11] HU J F, HUANG T Zh, DENG L J, et al. Fusformer: A transformer-based fusion network for hyperspectral image super-resolution[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.

    [12] [12] WU H P, XIAO B, CODELLA N, et al. Cvt: Introducing convolutions to vision transformers[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. New York, USA: IEEE Press, 2021: 22-31.

    [13] [13] HE A, LI T, LI N, et al. CABNet: Category attention block for imbalanced diabetic retinopathy grading[J]. IEEE Transactions on Medical Imaging, 2020, 40(1): 143-153.

    [14] [14] LI Zh W, SUI H, LUO C, et al. Morphological convolution and attention calibration network for hyperspectral and LiDAR data classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 5728-5740.

    [15] [15] PAN H D, GAO F, DONG J Y, et al. Multiscale adaptive fusion network for hyperspectral image denoising[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 3045-3059.

    [17] [17] SUI L Ch, LI L, LI J, et al. Fusion of hyperspectral and multispectral images based on a Bayesian nonparametric approach[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12: 1205-1218.

    [18] [18] CHAI T, DRAXLER R R. Root mean square error (RMSE) or mean absolute error (MAE)-Arguments against avoiding RMSE in the literature[J]. Geoscientific Model Development, 2014, 7(3): 1247-1250.

    [19] [19] JIANG J J, SUN H, LIU X M, et al. Learning spatial-spectral prior for super-resolution of hyperspectral imagery[J]. IEEE Transactions on Computational Imaging, 2020, 6: 1082-1096.

    [20] [20] LI Q, YUAN Y, JIA X P, et al. Dual-stage approach toward hyperspectral image super-resolution[J]. IEEE Transactions on Image Processing, 2022, 31: 7252-7263.

    [21] [21] WANG Zh, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.

    [22] [22] 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.

    [23] [23] CHAKRABARTI A, ZICKLER T. Statistics of real-world hyperspectral images[C]//CVPR 2011. New York, USA: IEEE Press, 2011: 193-200.

    [24] [24] WEI Q, BIOUCAS-DIAS J, DOBIGEON N, et al. Hyperspectral and multispectral image fusion based on a sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7): 3658-3668.

    [25] [25] LI Sh T, DIAN R W, FANG L Y, et al. Fusing hyperspectral and multispectral images via coupled sparse tensor factorization[J]. IEEE Transactions on Image Processing, 2018, 27(8): 4118-4130.

    [26] [26] DIAN R W, FANG L Y, LI Sh T. Hyperspectral image super-resolution via non-local sparse tensor factorization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2017: 5344-5353.

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    MA Fei, WANG Fang, HUO Shuai. Adaptive deep prior for hyperspectral image super-resolution[J]. Laser Technology, 2024, 48(4): 491

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    Paper Information

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    Received: Aug. 4, 2023

    Accepted: Dec. 2, 2024

    Published Online: Dec. 2, 2024

    The Author Email: WANG Fang (femircom@gmail.com)

    DOI:10.7510/jgjs.issn.1001-3806.2024.04.006

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