• Optics and Precision Engineering
  • Vol. 30, Issue 20, 2489 (2022)
Deqiang CHENG1, Jiamin ZHAO1, Qiqi KOU2,*, Liangliang CHEN1, and Chenggong HAN1
Author Affiliations
  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou226, China
  • 2School of Computer Science and Technology, China University of Mining and Technology, Xuzhou1116, China
  • show less
    DOI: 10.37188/OPE.20223020.2489 Cite this Article
    Deqiang CHENG, Jiamin ZHAO, Qiqi KOU, Liangliang CHEN, Chenggong HAN. Multi-scale dense feature fusion network for image super-resolution[J]. Optics and Precision Engineering, 2022, 30(20): 2489 Copy Citation Text show less
    References

    [1] 1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

    [2] K H GUO, H F GUO, S REN et al. Towards efficient motion-blurred public security video super-resolution based on back-projection networks. Journal of Network and Computer Applications, 166, 102691(2020).

    [3] 3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

    [4] W LU, Y P TAN. Color filter array demosaicking: new method and performance measures. IEEE Transactions on Image Processing, 12, 1194-1210(2003).

    [5] M IRANI, S PELEG. Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53, 231-239(1991).

    [6] 6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

    [7] 7董本志, 于明聪, 赵鹏. 基于小波域的图像超分辨率重建方法[J]. 液晶与显示, 2021, 36(2): 317-326. doi: 10.37188/CJLCD.2020-0101DONGB Z, YUM C, ZHAOP. Image super-resolution reconstruction based on wavelet domain[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(2): 317-326.(in Chinese). doi: 10.37188/CJLCD.2020-0101

    [8] 8陈宗航, 胡海龙, 姚剑敏, 等. 基于改进生成对抗网络的单帧图像超分辨率重建[J]. 液晶与显示, 2021, 36(5): 705-712. doi: 10.37188/CJLCD.2020-0250CHENZ H, HUH L, YAOJ M, et al. Single frame image super-resolution reconstruction based on improved generative adversarial network[J]. Chinese Journal of Liquid Crystals and Displays, 2021, 36(5): 705-712.(in Chinese). doi: 10.37188/CJLCD.2020-0250

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

    [10] 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).

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

    [12] 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).

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

    [14] H KIM et al. Enhanced deep residual networks for single image super-resolution, 1132-1140(2016).

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

    [16] Y TAI, J YANG, X M LIU. Image super-resolution via deep recursive residual network, 2790-2798(2017).

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

    [18] J C LI, F M FANG, K F MEI et al. Multi-scale Residual Network for Image Super-resolution. Computer Vision-ECCV 2018, 527-542(2018).

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

    [20] X Y HE, Z T MO, P S WANG et al. ODE-inspired network design for single image super-resolution, 1732-1741(2019).

    [21] L X LI, H S FENG, B ZHENG et al. DID: a nested dense in dense structure with variable local dense blocks for super-resolution image reconstruction, 2582-2589(2020).

    [22] R ZEYDE, M ELAD, M PROTTER. On single image scale-up using sparse-representations, 711-730(2010).

    [23] D MARTIN, C FOWLKES et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, 416-423(2001).

    [24] J B HUANG, A SINGH, N AHUJA. Single image super-resolution from transformed self-exemplars, 5197-5206(2015).

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

    [26] W S LAI, J B HUANG, N AHUJA et al. Deep Laplacian pyramid networks for fast and accurate super-resolution, 5835-5843(2017).

    [27] X T LUO, Y XIE, Y L ZHANG et al. LatticeNet: towards lightweight image super-resolution with lattice block, 2020, 279-289(2020).

    Deqiang CHENG, Jiamin ZHAO, Qiqi KOU, Liangliang CHEN, Chenggong HAN. Multi-scale dense feature fusion network for image super-resolution[J]. Optics and Precision Engineering, 2022, 30(20): 2489
    Download Citation