Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1028001(2025)

Lightweight Remote Sensing Image Super-Resolution Reconstruction Based on Saliency Analysis and Information Distillation

Xueli Shen, Xiaoming Zhu*, and Haibo Jin
Author Affiliations
  • School of Software, Liaoning Technical University, Huludao 125105, Liaoning , China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (20)
    Equations (18)
    References (31)
    Tools
    Paper Information
    Topics
    References(31)

    [1] Wang Q, Liu S T, Chanussot J et al. Scene classification with recurrent attention of VHR remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1155-1167(2019).

    [2] He D, Shi Q, Liu X P et al. Generating 2m fine-scale urban tree cover product over 34 metropolises in China based on deep context-aware sub-pixel mapping network[J]. International Journal of Applied Earth Observation and Geoinformation, 106, 102667(2022).

    [3] Wang J J, Zhong Y F, Zheng Z et al. RSNet: the search for remote sensing deep neural networks in recognition tasks[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 2520-2534(2021).

    [4] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[M]. Computer vision-ECCV 2016, 9906, 391-407(2016).

    [5] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [6] Liebel L, Körner M. Single-image super resolution for multispectral remote sensing data using convolutional neural networks[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 883-890(2016).

    [7] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

    [8] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).

    [9] Geng H W, Wang Y, Xin Y L. Depth image super-resolution reconstruction network based on dual feature fusion guidance[J]. Laser & Optoelectronics Progress, 61, 0837007(2024).

    [10] Lai W S, Huang J B, Ahuja N et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C], 5835-5843(2017).

    [11] Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network[M]. Computer vision-ECCV 2018, 11214, 256-272(2018).

    [12] Tian C W, Xu Y, Zuo W M et al. Coarse-to-fine CNN for image super-resolution[J]. IEEE Transactions on Multimedia, 23, 1489-1502(2020).

    [13] Hui Z, Gao X B, Yang Y C et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-2032(2019).

    [14] Liu J, Tang J, Wu G S. Residual feature distillation network for lightweight image super-resolution[M]. Computer vision-ECCV 2020 workshops, 13537, 41-55(2020).

    [15] Yi J B, Chen J K, Cao F et al. Design of lightweight re-parameterized remote sensing image super-resolution network[J]. Optics and Precision Engineering, 32, 268-285(2024).

    [16] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[M]. Computer vision-ECCV 2018, 11211, 294-310(2018).

    [17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

    [18] Zhang Y, Sun M L, Sun Y M et al. Image super-resolution reconstruction algorithm based on adaptive two-branch block[J]. Laser & Optoelectronics Progress, 61, 1037002(2024).

    [19] Liang J Y, Cao J Z, Sun G L et al. SwinIR: image restoration using swin transformer[C], 1833-1844(2021).

    [20] Li X, Zhang J. Lightweight image super-resolution based on shuffle group convolution and sparse global attention[J]. Laser & Optoelectronics Progress, 61, 0411003(2024).

    [21] Ma N N, Zhang X Y, Liu M et al. Activate or not: learning customized activation[C], 8028-8038(2021).

    [22] Crnjanski J, Krstić M, Totović A et al. Adaptive sigmoid-like and PReLU activation functions for all-optical perceptron[J]. Optics Letters, 46, 2003-2006(2021).

    [23] Tian R, Sun G, Liu X et al. Sobel edge detection based on weighted nuclear norm minimization image denoising[J]. Electronics, 10, 655(2021).

    [24] Gonzalez R C[M]. Digital image processing(2009).

    [25] Haase D, Amthor M. Rethinking depthwise separable convolutions: how intra-kernel correlations lead to improved MobileNets[C], 14588-14597(2020).

    [26] Wu H L, Ni N, Zhang L B. Lightweight stepless super-resolution of remote sensing images via saliency-aware dynamic routing strategy[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5601717(2023).

    [27] Tai Y, Yang J, Liu X M. Image super-resolution via deep recursive residual network[C], 2790-2798(2017).

    [28] Wang S Z, Zhou T F, Lu Y et al. Contextual transformation network for lightweight remote-sensing image super-resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5615313(2022).

    [29] Wang Z Y, Li L L, Xue Y et al. FeNet: feature enhancement network for lightweight remote-sensing image super-resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5622112(2022).

    [30] Tian C W, Zhang X Y, Zhang Q et al. Image super-resolution via dynamic network[J]. CAAI Transactions on Intelligence Technology, 9, 837-849(2024).

    [31] Wu G, Jiang J J, Jiang K et al. Fully 1×1 convolutional network for lightweight image super-resolution[EB/OL]. https://arxiv.org/abs/2307.16140v2

    Tools

    Get Citation

    Copy Citation Text

    Xueli Shen, Xiaoming Zhu, Haibo Jin. Lightweight Remote Sensing Image Super-Resolution Reconstruction Based on Saliency Analysis and Information Distillation[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1028001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 6, 2024

    Accepted: Oct. 28, 2024

    Published Online: May. 9, 2025

    The Author Email: Xiaoming Zhu (1945595956@qq.com)

    DOI:10.3788/LOP241966

    CSTR:32186.14.LOP241966

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology