ZHAO Shengrong, LYU Zehua, LIANG Hu, Mudar SAREM. A Mixed Non-local Prior Model for Image Super-resolution Reconstruction[J]. Chinese Journal of Electronics, 2017, 26(4): 778-783. doi: 10.1049/cje.2016.06.024
Citation: ZHAO Shengrong, LYU Zehua, LIANG Hu, Mudar SAREM. A Mixed Non-local Prior Model for Image Super-resolution Reconstruction[J]. Chinese Journal of Electronics, 2017, 26(4): 778-783. doi: 10.1049/cje.2016.06.024

A Mixed Non-local Prior Model for Image Super-resolution Reconstruction

doi: 10.1049/cje.2016.06.024
Funds:  This work is supported by the Hubei Province Natural Science Foundation (No.2013CFB152), and partly supported by the Ph.D. Programs Foundation of Ministry of Education of China (No.20120142120110).
More Information
  • Corresponding author: LYU Zehua (corresponding author) was born in Yichang, Hubei Province, China. He received the Ph.D. degree in the School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. He is now a lecturer in the School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests include Image Processing and Artificial Intelligence. (Email: lvzehua@hust.edu.cn)
  • Received Date: 2015-06-24
  • Rev Recd Date: 2015-09-25
  • Publish Date: 2017-07-10
  • Generating high-resolution image from a set of degraded low-resolution images is a challenge problem in image processing. Due to the ill-posed nature of Super-resolution (SR), it is necessary to find an effective image prior model to make it well-posed. For this purpose, we propose a mixed non-local prior model by adaptively combining the non-local total variation and non-local H1 models, and establish a multi-frame SR method based on this mixed non-local prior model. The unknown Highresolution (HR) image, motion parameters and hyperparameters related to the new prior model and noise statistics are determined automatically, resulting in an unsupervised super-resolution method. Extensive experiments demonstrate the effectiveness of the proposed SR method, which can not only preserve image details better but also suppress noise better.
  • loading
  • R. Bahy, G. Salama and T. Mahmoud, “Adaptive regularization based super resolution reconstruction technique for multifocus low-resolution images”, Signal Processing, Vol.103, No.SI, pp.155-167, 2014.
    M. Zibetti, F. Bazan and J. Mayer, “Estimation of the parameters in regularized simultaneous super-resolution”, Pattern Recognition Letters, Vol.32, No.1, pp.69-78, 2011.
    Z. Li, Q. Chen, Q. Peng, Q. Zhang and W. Li, “MAP-based single-frame super-resolution reconstruction for character image”, Acta Electronica Sinica, Vol.43, No.1, pp.191-197, 2015. (in Chinese).
    F. Lucka and M. Burger, “Maximum a posteriori estimates in linear inverse problems with log-concave priors are proper Bayes estimators”, Inverse Problems, Vol.30, No.11, Article ID 114004, 21 pages, 2014.
    S. Babacan, R. Molina and A. Katsaggelos, “Variational Bayesian super resolution”, IEEE Transactions on Image Processing, Vol.20, No.4, pp.984-999, 2011.
    S. Farsiu, M. Robinson, M. Elad and P. Milanfar, “Fast and robust multiframe super resolution”, IEEE Transactions on Image Processing, Vol.13, No.10, pp.1327-1344, 2004.
    S. Villena, M. Vega, S. Babacan, R. Molina and A. Katsaggelos, “Bayesian combination of sparse and non-sparse priors in image super resolution”, Digital Signal Processing, Vol.23, No.2, pp.530-541, 2013.
    C. Chen, H. Liang, S. Zhao, Z. Lyu and M. Sarem, “A novel reconstruction model for multi-frame super-resolution image based on lmix prior”, Computers and Electrical Engineering, Vol.40, No.8, pp.142-153, 2014.
    L. Li, Y. Xie, W. Hu and W. Zhang, “Single image superresolution using combined total variation regularization by split Bregman Iteration”, Neurocomputing, Vol.142, No.SI, pp.551-560, 2014.
    S. Kindermann, S. Osher and P. Jones, “Deblurring and denoising of images by nonlocal functionals”, Multiscale Modeling and Simulation, Vol.4, No.4, pp.1091-1115, 2005.
    S. Son, D. Kang and S. Yoo, “Non-local huber regularization for image denoising: A hybrid approach of two non-local regularizations”, 2nd International Conference on Pattern Recognition Applications and Methods, Barcelona, Spain, pp.554-559, 2013.
    B. Ning, J. Li and X. Gao, “Fast algorithms of image fusion for super-resolution reconstruction from multiple images with random shifts”, Chinese Journal of Electronics, Vol.21, No.1, pp.69-72, 2012.
    S. Wang, L. Zhuo and X. Li, “A panchromatic image-based spectral imagery super resolution algorithm”, Chinese Journal of Electronics, Vol.20, No.4, pp.617-620, 2011.
    J. Darbon, A. Cunha, T. Chan, S. Osher and G. Jensen, “Fast nonlocal filtering applied to electron cryomicroscopy”, IEEE International Symposium on Biomedical Imaging: From Macro to Nano, Paris, France, pp.1331-1334, 2008.
    S. Babacan, R. Molina and A. Katsaggelos, “Variational Bayesian blind deconvolution using a total variation prior”, IEEE Transactions on Image Processing, Vol.18, No.1, pp.12-26, 2009.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (124) PDF downloads(577) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return