Journal of Applied Optics, Volume. 44, Issue 6, 1343(2023)
Image super-resolution reconstruction based on multi-scale two-stage network
[1] Zhenwei SHI, Sen LEI. Overview of image super resolution reconstruction algorithms. Data Acquisition and Processing, 35, 1-20(2020).
[2] B Y MENG, J W HONG, Y L MENG et al. Overview of research on image super-resolution reconstruction, 131-135(2021).
[3] C DONG, C C LOY, K HE et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2015).
[4] C DONG, C C LOY, X TANG. Accelerating the super-resolution convolutional neural network, 391-407(2016).
[5] W 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).
[6] W WANG, Y HU, Y LUO et al. Brief survey of single image super-resolution reconstruction based on deep learning approaches. Sensing and Imaging, 21, 1-20(2020).
[7] K HE, X ZHANG, S REN et al. Deep residual learning for image recognition, 770-778(2016).
[8] J KIM, J K LEE, K M LEE. Accurate image super-resolution using very deep convolutional networks, 1646-1654(2016).
[9] J KIM, J K LEE, K M LEE. Deeply-recursive convolutional network for image super-resolution, 1637-1645(2016).
[11] Dengwen ZHOU, Lijuan ZHAO, Ran DUAN et al. Image super-resolution reconstruction based on recursive residual networks. Journal of Automation, 45, 1157-1165(2019).
[12] Jichang GUO, Jie WU, Chunle GUO et al. Image super-resolution reconstruction based on residual connection convolutional neural network. Journal of Jilin University (Engineering Edition), 49, 1726-173(2019).
[13] B LIM, S SON, H KIM et al. Enhanced deep residual networks for single image super-resolution, 136-144(2017).
[14] Min ZHANG, Gang HUANG, Qichao CHEN. Image super-resolution reconstruction method based on residual learning. Computer Technology and Development, 31, 51-56(2021).
[15] Jian ZHANG, Jingxuan HE, Rong WANG. Image super-resolution reconstruction algorithm based on CNN and Resblock. Information Technology and Network Security, 38, 54-59(2019).
[16] Y ZHANG, Y TIAN, Y KONG et al. Residual dense network for image super-resolution, 2472-2481(2018).
[17] Y ZHANG, K LI, K LI et al. Image super-resolution using very deep residual channel attention networks, 286-301(2018).
[18] Jingbo WEI. Super-resolution reconstruction of residual dense attention networks. Electronic Technology and Software Engineering, 127-128(2021).
[19] T DAI, J CAI, Y ZHANG et al. Second-order attention network for single image super-resolution, 11065-11074(2019).
[20] B NIU, W WEN, W REN et al. Single image super-resolution via a holistic attention network, 191-207(2020).
[21] Tao LI, Xiucheng DONG, Hongwei LIN. Deep image super-resolution reconstruction based on deep supervised cross scale attention network. Journal of Electronics, 51, 128-138(2023).
[22] Y FEI, F H LIAN, Y YAN. An improved PSNR algorithm for objective video quality evaluation, 376-380(2007).
[23] Z WANG, A C BOVIK, H R SHEIKH et al. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612(2004).
[24] M S GREESHMA, V R BINDU. Super-resolution using deep networks for chest X-ray images, 198-201(2021).
[25] S WOO, J PARK, J Y LEE et al. CBAM: convolutional block attention module, 3-19(2018).
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
Category: Research Articles
Received: Jan. 16, 2023
Accepted: --
Published Online: Mar. 12, 2024
The Author Email: YIN Lexuan (尹乐璇)