• Acta Optica Sinica
  • Vol. 37, Issue 12, 1210004 (2017)
Chao Sun1、*, Junwei Lü1, Jianwei Li2, and Rongchao Qiu1
Author Affiliations
  • 1 Department of Control Engineering, Naval Aeronautical University, Yantai, Shandong 264001, China
  • 2 Department of Electronic and Information Engineering, Naval Aeronautical University, Yantai, Shandong 264001, China
  • show less
    DOI: 10.3788/AOS201737.1210004 Cite this Article Set citation alerts
    Chao Sun, Junwei Lü, Jianwei Li, Rongchao Qiu. Method of Rapid Image Super-Resolution Based on Deconvolution[J]. Acta Optica Sinica, 2017, 37(12): 1210004 Copy Citation Text show less
    References

    [1] Su Heng, Zhou Jie, Zhang Zhihao. Survey of super-resolution image reconstruction methods[J]. Acta Automatica Sinica, 39, 1202-1213(2013).

    [2] Chang H, Yeung D Y, Xiong Y. Super-resolution through neighbor embedding[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1, 275-282(2004).

    [3] Timofte R, de Smet V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution[C]. IEEE International Conference on Computer Vision, 1920-1927(2013).

    [4] Timofte R, de Smet V, van Gool L. A+: Adjusted anchored neighborhood regression for fast super-resolution[C]. Asian Conference on Computer Vision, 9006, 111-126(2014).

    [5] Yang J, Wright J, Huang T et al. Image super resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010). http://europepmc.org/abstract/MED/20483687

    [6] Yang J, Wright J, Ma Y et al. Image super-resolution as sparse representation of raw image patches[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1-8(2008).

    [7] Dong C, Loy C C, He K et al. Learning a deep convolutional network for image super-resolution[C]. European Conference on Computer Vision, 184-199(2014).

    [8] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1646-1654(2016).

    [9] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]. IEEE Conference on Computer Vision and Pattern Recognition, 1637-1645(2016).

    [10] Zheng Xiangtao, Yuan Yuan, Lu Xiaoqiang. Single image super-resolution restoration algorithm from external example to internal self-similarity[J]. Acta Optica Sinica, 37, 0318006(2017).

    [11] Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]. European Conference on Computer Vision, 391-407(2016).

    [12] 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]. IEEE Conference on Computer Vision and Pattern Recognition, 1874-1883(2016).

    [13] Dong C, Chen C L, He K et al. Image super-resolution using deep convolutional networks[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [14] Chen Y, Pock T. Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1256-1272(2016). http://ieeexplore.ieee.org/document/7527621

    [15] Simonyan K. 2017-06-10][J/OL]. Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer Science(2014). https://arxiv.org/pdf/1409.1556.pdf.

    [16] He K, Zhang X, Ren S et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1906(2015). http://www.sciencedirect.com/science/article/pii/S0031320315004252

    [17] Martin D, Fowlkes C, Tal D et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]. IEEE International Conference on Computer Vision, 416-423(2001).

    [18] Schulter S, Leistner C, Bischof H. Fast and accurate image upscaling with super-resolution forests[C]. IEEE Conference on Computer Vision and Pattern Recognition, 3791-3799(2015).

    [19] Wang Z, Liu D, Yang J et al. Deeply improved sparse coding for image super-resolution[C]. IEEE International Conference on Computer Vision, 370-378(2015).

    [20] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]. IEEE Conference on Computer Vision and Pattern Recognition, 5197-5206(2015).

    CLP Journals

    [1] Jinghui Chu, Fengshuo Hu, Jiaqi Zhang, Wei Lü. An Improved Single-Frame Super-Resolution Algorithm for Magnetic Resonance Image[J]. Laser & Optoelectronics Progress, 2018, 55(5): 051009

    [2] Cheng Deqiang, Cai Yingchun, Chen Liangliang, Song Yulong. Multi-Scale Convolutional Neural Network Reconstruction Algorithm Based on Edge Correction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 91003

    Chao Sun, Junwei Lü, Jianwei Li, Rongchao Qiu. Method of Rapid Image Super-Resolution Based on Deconvolution[J]. Acta Optica Sinica, 2017, 37(12): 1210004
    Download Citation