Journal of Applied Optics, Volume. 44, Issue 6, 1343(2023)

Image super-resolution reconstruction based on multi-scale two-stage network

Qingjiang CHEN, Lexuan YIN*, and Luoyi SHAO
Author Affiliations
  • School of Science, Xi'an University of Architecture and Technology, Xi'an 710055, China
  • show less
    References(26)

    [1] SHI Zhenwei, LEI Sen. Overview of image super resolution reconstruction algorithms[J]. Data Acquisition and Processing, 35, 1-20(2020).

    [2] MENG B Y, HONG J W, MENG Y L et al. Overview of research on image super-resolution reconstruction[C], 131-135(2021).

    [3] DONG C, LOY C C, HE K et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2015).

    [4] DONG C, LOY C C, TANG X. Accelerating the super-resolution convolutional neural network[C], 391-407(2016).

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

    [6] WANG W, HU Y, LUO Y et al. Brief survey of single image super-resolution reconstruction based on deep learning approaches[J]. Sensing and Imaging, 21, 1-20(2020).

    [7] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition[C], 770-778(2016).

    [8] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

    [9] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C], 1637-1645(2016).

    [11] ZHOU Dengwen, ZHAO Lijuan, DUAN Ran et al. Image super-resolution reconstruction based on recursive residual networks[J]. Journal of Automation, 45, 1157-1165(2019).

    [12] GUO Jichang, WU Jie, GUO Chunle et al. Image super-resolution reconstruction based on residual connection convolutional neural network[J]. Journal of Jilin University (Engineering Edition), 49, 1726-173(2019).

    [13] LIM B, SON S, KIM H et al. Enhanced deep residual networks for single image super-resolution[C], 136-144(2017).

    [14] ZHANG Min, HUANG Gang, CHEN Qichao. Image super-resolution reconstruction method based on residual learning[J]. Computer Technology and Development, 31, 51-56(2021).

    [15] ZHANG Jian, HE Jingxuan, WANG Rong. Image super-resolution reconstruction algorithm based on CNN and Resblock[J]. Information Technology and Network Security, 38, 54-59(2019).

    [16] ZHANG Y, TIAN Y, KONG Y et al. Residual dense network for image super-resolution[C], 2472-2481(2018).

    [17] ZHANG Y, LI K, LI K et al. Image super-resolution using very deep residual channel attention networks[C], 286-301(2018).

    [18] WEI Jingbo. Super-resolution reconstruction of residual dense attention networks[J]. Electronic Technology and Software Engineering, 127-128(2021).

    [19] DAI T, CAI J, ZHANG Y et al. Second-order attention network for single image super-resolution[C], 11065-11074(2019).

    [20] NIU B, WEN W, REN W et al. Single image super-resolution via a holistic attention network[C], 191-207(2020).

    [21] LI Tao, DONG Xiucheng, LIN Hongwei. Deep image super-resolution reconstruction based on deep supervised cross scale attention network[J]. Journal of Electronics, 51, 128-138(2023).

    [22] FEI Y, LIAN F H, YAN Y. An improved PSNR algorithm for objective video quality evaluation[C], 376-380(2007).

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

    [24] GREESHMA M S, BINDU V R. Super-resolution using deep networks for chest X-ray images[C], 198-201(2021).

    [25] WOO S, PARK J, LEE J Y et al. CBAM: convolutional block attention module[C], 3-19(2018).

    Tools

    Get Citation

    Copy Citation Text

    Qingjiang CHEN, Lexuan YIN, Luoyi SHAO. Image super-resolution reconstruction based on multi-scale two-stage network[J]. Journal of Applied Optics, 2023, 44(6): 1343

    Download Citation

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

    Category: Research Articles

    Received: Jan. 16, 2023

    Accepted: --

    Published Online: Mar. 12, 2024

    The Author Email: Lexuan YIN (尹乐璇)

    DOI:10.5768/JAO202344.0602004

    Topics