On Algorithms for Automatic Deblurring from a Single Image

On Algorithms for Automatic Deblurring from a Single Image

Year:    2012

Journal of Computational Mathematics, Vol. 30 (2012), Iss. 1 : pp. 80–100

Abstract

In this paper, we study two variational blind deblurring models for a single image. The first model is to use the total variation prior in both image and blur, while the second model is to use the frame based prior in both image and blur. The main contribution of this paper is to show how to employ the generalized cross validation (GCV) method efficiently and automatically to estimate the two regularization parameters associated with the priors in these two blind motion deblurring models. Our experimental results show that the visual quality of restored images by the proposed method is very good, and they are competitive with the tested existing methods. We will also demonstrate the proposed method is also very efficient.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1110-m11si13

Journal of Computational Mathematics, Vol. 30 (2012), Iss. 1 : pp. 80–100

Published online:    2012-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    21

Keywords:    Blind deconvolution Iterative methods Total variation Framelet Generalized cross validation.

  1. Restoration of motion blurred image using Lucy Richardson Algorithm

    Mahapatra, Abhilash | Faruquee, Muddasir Ahamad | Kumar, Pranaw

    2018 International Conference on Communication, Computing and Internet of Things (IC3IoT), (2018), P. 73

    https://doi.org/10.1109/IC3IoT.2018.8668143 [Citations: 3]
  2. ABLUR: An FPGA-based adaptive deblurring core for real-time applications

    Farulla, Giuseppe Airo | Indaco, Marco | Prinetto, Paolo | Rolfo, Daniele | Trotta, Pascal

    2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), (2014), P.104

    https://doi.org/10.1109/AHS.2014.6880165 [Citations: 0]
  3. A cartoon-plus-texture image decomposition model for blind deconvolution

    Wang, Wei | Zhao, Xile | Ng, Michael

    Multidimensional Systems and Signal Processing, Vol. 27 (2016), Iss. 2 P.541

    https://doi.org/10.1007/s11045-015-0318-7 [Citations: 19]
  4. Convex regularized inverse filtering methods for blind image deconvolution

    Wang, Wei | Ng, Michael K.

    Signal, Image and Video Processing, Vol. 10 (2016), Iss. 7 P.1353

    https://doi.org/10.1007/s11760-016-0924-3 [Citations: 12]
  5. A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints

    Chen, Ke | Harding, Simon P. | Williams, Bryan M. | Zheng, Yalin

    International Journal of Computational Mathematics, Vol. 2015 (2015), Iss. P.1

    https://doi.org/10.1155/2015/860263 [Citations: 4]
  6. Image Completion and Blind Deconvolution: Model and Algorithm

    Lin, Xue-lei | Ng, Michael K.

    Journal of Scientific Computing, Vol. 89 (2021), Iss. 3

    https://doi.org/10.1007/s10915-021-01554-4 [Citations: 0]
  7. Iteratively Reweighted Blind Deconvolution With Adaptive Regularization Parameter Estimation

    Fang, Houzhang | Chang, Yi | Zhou, Gang | Deng, Lizhen

    IEEE Access, Vol. 5 (2017), Iss. P.11959

    https://doi.org/10.1109/ACCESS.2017.2719119 [Citations: 7]
  8. Accelerating Existing Non-Blind Image Deblurring Techniques through a Strap-On Limited-Memory Switched Broyden Method

    LAHOULI, Ichraf | HAELTERMAN, Robby | DEGROOTE, Joris | SHIMONI, Michal | DE CUBBER, Geert | ATTIA, Rabah

    IEICE Transactions on Information and Systems, Vol. E101.D (2018), Iss. 5 P.1288

    https://doi.org/10.1587/transinf.2017MVP0022 [Citations: 2]
  9. Hybrid Variational Model for Texture Image Restoration

    Ma, Liyan | Zeng, Tieyong | Li, Gongyan

    East Asian Journal on Applied Mathematics, Vol. 7 (2017), Iss. 3 P.629

    https://doi.org/10.4208/eajam.090217.300617a [Citations: 4]
  10. Evaluation of image deblurring algorithms for real-time applications

    Farulla, Giuseppe Airo | Indaco, Marco | Rolfo, Daniele | Russo, Ludovico Orlando | Trotta, Pascal

    2014 9th IEEE International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS), (2014), P.1

    https://doi.org/10.1109/DTIS.2014.6850668 [Citations: 1]
  11. Blurring prediction in monocular SLAM

    Russo, Ludovico Orlando | Farulla, Giuseppe Airo | Indaco, Marco | Rosa, Stefano | Rolfo, Daniele | Bona, Basilio

    2013 8th IEEE Design and Test Symposium, (2013), P.1

    https://doi.org/10.1109/IDT.2013.6727095 [Citations: 9]
  12. Deblurring and Sparse Unmixing for Hyperspectral Images

    Zhao, Xi-Le | Wang, Fan | Huang, Ting-Zhu | Ng, Michael K. | Plemmons, Robert J.

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 51 (2013), Iss. 7 P.4045

    https://doi.org/10.1109/TGRS.2012.2227764 [Citations: 143]
  13. Variational model for simultaneously image denoising and contrast enhancement

    Wang, Wei | Zhang, Caixia | Ng, Michael K.

    Optics Express, Vol. 28 (2020), Iss. 13 P.18751

    https://doi.org/10.1364/OE.28.018751 [Citations: 15]