Optics and Precision Engineering, Volume. 32, Issue 2, 268(2024)

Design of lightweight re-parameterized remote sensing image super-resolution network

Jianbing YI*, Junkuan CHEN, Feng CAO, Jun LI, and Weijia XIE
Author Affiliations
  • College of Information Engineering, Jiangxi University of Science and Technology, Ganzhou341000,China
  • show less
    References(45)

    [1] [1] 刘雪岩, 许聿达, 雷建昕, 等. 基于视差放大与超分辨率的三维光场腹腔镜标定[J]. 光学 精密工程, 2022, 30(5): 510-517. doi: 10.37188/OPE.2021.0332LIUX Y, XUY D, LEIJ X, et al. Three-dimensional light field endoscope calibration based on light field disparity amplifier and super-resolution network[J]. Opt. Precision Eng., 2022, 30(5): 510-517.(in Chinese). doi: 10.37188/OPE.2021.0332

    [2] P RASTI, T UIBOUPIN, S ESCALERA et al. Convolutional neural network super resolution for face recognition in surveillance monitoring, 175-184(2016).

    [3] [3] 王延文, 雷为民, 张伟, 等. 基于生成模型的视频图像重建方法综述[J]. 通信学报, 2022, 43(9): 194-208. doi: 10.11959/j.issn.1000-436x.2022178WANGY W, LEIW M, ZHANGW, et al. Survey on video image reconstruction method based on generative model[J]. Journal on Communications, 2022, 43(9): 194-208.(in Chinese). doi: 10.11959/j.issn.1000-436x.2022178

    [4] H ZENG, J R CAI, L D LI et al. Learning image-adaptive 3D lookup tables for high performance photo enhancement in real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 2058-2073(2022).

    [5] [5] 耿铭昆, 吴凡路, 王栋. 轻量化火星遥感影像超分辨率重建网络[J]. 光学 精密工程, 2022, 30(12): 1487-1498. doi: 10.37188/OPE.20223012.1487GENGM K, WUF L, WANGD. Lightweight Mars remote sensing image super-resolution reconstruction network[J]. Opt. Precision Eng., 2022, 30(12): 1487-1498.(in Chinese). doi: 10.37188/OPE.20223012.1487

    [6] [6] 王娟, 刘子杉, 武明虎, 等. 融合超分辨率重建技术的多尺度目标检测算法[J]. 西安电子科技大学学报, 2023, 50(3): 122-131.WANGJ, LIUZ S, WUM H, et al. Multi-scale object detection algorithm combined with super-resolution reconstruction technology[J]. Journal of Xidian University, 2023, 50(3): 122-131.(in Chinese)

    [7] G CHENG, J W HAN, X Q LU. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105, 1865-1883(2017).

    [8] R KEYS. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153-1160(1981).

    [9] [9] 潘璐璐, 延伟东, 郑红婵. 基于多尺度局部自相似性和邻域嵌入的超分辨率算法研究[J]. 西北工业大学学报, 2015, 33(6): 1014-1019. doi: 10.3969/j.issn.1000-2758.2015.06.026PANL L, YANW D, ZHENGH C. Super resolution based on multi-scale local self-similarity and neighbor embedding[J]. Journal of Northwestern Polytechnical University, 2015, 33(6): 1014-1019.(in Chinese). doi: 10.3969/j.issn.1000-2758.2015.06.026

    [10] [10] 朱福珍, 刘越, 黄鑫, 等. 改进的稀疏表示遥感图像超分辨重建[J]. 光学 精密工程, 2019, 27(3): 718-725. doi: 10.13482/j.issn1001-7011.2019.05.004ZHUF Z, LIUY, HUANGX, et al. Remote sensing image super-resolution based on improved sparse representation[J]. Opt. Precision Eng., 2019, 27(3): 718-725.(in Chinese). doi: 10.13482/j.issn1001-7011.2019.05.004

    [11] R TIMOFTE, V DE SMET, L VAN GOOL. A+ Adjusted Anchored Neighborhood Regression for Fast Super-Resolution. Computer Vision -- ACCV 2014, 111-126(2015).

    [12] C DONG, C C LOY, K M HE et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [13] [13] 蔡体健, 彭潇雨, 石亚鹏, 等. 通道注意力与残差级联的图像超分辨率重建[J]. 光学 精密工程, 2021, 29(1): 142-151. doi: 10.37188/OPE.20212901.0142CAIT J, PENGX Y, SHIY P, et al. Channel attention and residual concatenation network for image super-resolution[J]. Opt. Precision Eng., 2021, 29(1): 142-151.(in Chinese). doi: 10.37188/OPE.20212901.0142

    [14] [14] 程德强, 赵佳敏, 寇旗旗, 等. 多尺度密集特征融合的图像超分辨率重建[J]. 光学 精密工程, 2022, 30(20): 2489-2500. doi: 10.37188/OPE.20223020.2489CHENGD Q, ZHAOJ M, KOUQ Q, et al. Multi-scale dense feature fusion network for image super-resolution[J]. Opt. Precision Eng., 2022, 30(20): 2489-2500.(in Chinese). doi: 10.37188/OPE.20223020.2489

    [15] J M HAUT, M E PAOLETTI, R FERNANDEZ-BELTRAN et al. Remote sensing single-image superresolution based on a deep compendium model. IEEE Geoscience and Remote Sensing Letters, 16, 1432-1436(2019).

    [16] Z X PAN, W MA, J Y GUO et al. Super-resolution of single remote sensing image based on residual dense backprojection networks. IEEE Transactions on Geoscience and Remote Sensing, 57, 7918-7933(2019).

    [17] X Y DONG, X SUN, X P JIA et al. Remote sensing image super-resolution using novel dense-sampling networks. IEEE Transactions on Geoscience and Remote Sensing, 59, 1618-1633(2021).

    [18] S LEI, ZH W SHI. Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-10(2022).

    [19] S ARORA, N COHEN, E HAZAN. On the optimization of deep networks: implicit acceleration by overparameterization, 244-253(2018).

    [20] X H DING, Y C GUO, G G DING et al. ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks, 1911-1920(2019).

    [21] X H DING, X Y ZHANG, N N MA et al. RepVGG: making vgg-style convnets great again, 13728-13737(2021).

    [22] X D ZHANG, H ZENG, L ZHANG. Edge-oriented convolution block for real-time super resolution on mobile devices, 4034-4043(2021).

    [23] T L ZHANG, C J BIAN, X M ZHANG et al. Lightweight remote-sensing image super-resolution via re-parameterized feature distillation network. IEEE Geoscience and Remote Sensing Letters, 20, 6003905(2023).

    [24] X L WANG, R GIRSHICK, A GUPTA et al. Non-local neural networks, 7794-7803(2018).

    [25] J HU, L SHEN, G SUN. Squeeze-and-excitation networks, 7132-7141(2018).

    [26] Y CAO, J R XU, S LIN et al. GCNet: Non-local networks meet squeeze-excitation networks and beyond, 1971-1980(2019).

    [27] H J LIU, F Q LIU, X Y FAN et al. Polarized self-attention: towards high-quality pixel-wise mapping. Neurocomputing, 506, 158-167(2022).

    [28] L ZHU, X J WANG, Z H KE et al. BiFormer: vision transformer with bi-level routing attention, 10323-10333(2023).

    [29] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874-1883(2016).

    [30] F Y KONG, M X LI, S W LIU et al. Residual local feature network for efficient super-resolution, 765-775(2022).

    [31] J LIU, W J ZHANG, Y T TANG et al. Residual feature aggregation network for image super-resolution, 2356-2365(2020).

    [32] Y YANG, S NEWSAM. Bag-of-visual-words and spatial extensions for land-use classification, 270-279(2010).

    [33] J C YANG, J WRIGHT, T S HUANG et al. Image super-resolution via sparse representation. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 19, 2861-2873(2010).

    [34] C DONG, C C LOY, X O TANG. Accelerating the Super-Resolution Convolutional Neural Network. Computer Vision-ECCV 2016, 391-407(2016).

    [35] S LEI, Z W SHI, Z X ZOU. Super-resolution for remote sensing images via local-global combined network. IEEE Geoscience and Remote Sensing Letters, 14, 1243-1247(2017).

    [36] SH ZH WANG, T F ZHOU, Y LU et al. Contextual Transformation Network for Lightweight Remote-Sensing Image Super Resolution. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13(2022).

    [37] B KANG, K A SOHN. Fast Accurate and Lightweight Super-Resolution with Cascading Residual Network. Computer Vision-ECCV 2018, 256-272(2018).

    [38] Z HUI, X B GAO, Y C YANG et al. Lightweight image super-resolution with information multi-distillation network, 2024-2032(2019).

    [39] J LIU, J TANG, G S WU. Residual Feature Distillation Network for Lightweight Image Super-Resolution. Computer Vision-ECCV 2020 Workshops, 41-55(2020).

    [40] Q J WANG, S WANG, M F CHEN et al. DARN: Distance Attention Residual Network for Lightweight Remote-Sensing Image Super resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 714-724(2023).

    [41] K DABOV, V KATKOVNIK et al. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

    [42] M AHARON, M ELAD, A BRUCKSTEIN. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54, 4311-4322(2006).

    [43] D ZORAN, Y WEISS. From learning models of natural image patches to whole image restoration, 479-486(2011).

    [44] S GUO, Z F YAN, K ZHANG et al. Toward convolutional blind denoising of real photographs, 1712-1722(2019).

    [45] S ANWAR, N BARNES. Real image denoising with feature attention, 3155-3164(2019).

    Tools

    Get Citation

    Copy Citation Text

    Jianbing YI, Junkuan CHEN, Feng CAO, Jun LI, Weijia XIE. Design of lightweight re-parameterized remote sensing image super-resolution network[J]. Optics and Precision Engineering, 2024, 32(2): 268

    Download Citation

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

    Category:

    Received: Jul. 5, 2023

    Accepted: --

    Published Online: Apr. 2, 2024

    The Author Email: YI Jianbing (yijianbing8@jxust.edu.cn)

    DOI:10.37188/OPE.20243202.0268

    Topics