Robust defocus blur identification in the context of blind image quality assessment

被引:13
|
作者
Marais, Fzak van Zyl [1 ]
Steyn, Willem Herman [1 ]
机构
[1] Univ Stellenbosch, ZA-7600 Stellenbosch, South Africa
关键词
quality estimation; blind deconvolution; blur estimation;
D O I
10.1016/j.image.2007.06.003
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A defocus blur metric for use in blind image quality assessment is proposed. Blind image deconvolution methods are used to determine the metric. Existing direct deconvolution methods based on the cepstrum, bicepstrum and on a spectral subtraction technique are compared across 210 images. A variation of the spectral subtraction method, based on a power spectrum surface of revolution, is proposed and is found to compare favourably with existing direct deconvolution methods for defocus blur identification. The method is found to be especially useful when distinguishing between in-focus and out-of-focus images. (C) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:833 / 844
页数:12
相关论文
共 50 条
  • [41] Blind quality assessment of image and video based on fragile watermarking and robust features
    Bhattacharya, Ankan
    Palit, Sarbani
    MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING, 2018, 29 (04) : 1679 - 1709
  • [42] Blind quality assessment of image and video based on fragile watermarking and robust features
    Ankan Bhattacharya
    Sarbani Palit
    Multidimensional Systems and Signal Processing, 2018, 29 : 1679 - 1709
  • [43] Predicting the effects of defocus blur on contrast sensitivity with a model-based metric of retinal image quality
    Leroux, Charles
    Fontvieille, Christophe
    Leahy, Conor
    Marc, Isabelle
    Bardin, Fabrice
    JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2022, 39 (10) : 1866 - 1873
  • [44] No-reference assessment of blur and noise impacts on image quality
    Erez Cohen
    Yitzhak Yitzhaky
    Signal, Image and Video Processing, 2010, 4 : 289 - 302
  • [45] IMAGE BLUR CLASSIFICATION AND BLUR USEFULNESS ASSESSMENT
    Fan, Mingyuan
    Huang, Rui
    Feng, Wei
    Sun, Lizhou
    2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA & EXPO WORKSHOPS (ICMEW), 2017,
  • [46] No-reference image quality assessment using blur and noise
    Choi, Min Goo
    Jung, Jung Hoon
    Jeon, Jae Wook
    World Academy of Science, Engineering and Technology, 2009, 38 : 163 - 167
  • [47] Efficient discrete spatial techniques for blur support identification in blind image deconvolution
    Chen, L
    Yap, KH
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (04) : 1557 - 1562
  • [48] Identification of blur support size in blind image restoration with moderate/intense noise
    Zhang Chunxiao
    Zhao Yan
    Xu Dong
    SEVENTH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND CONTROL TECHNOLOGY: OPTOELECTRONIC TECHNOLOGY AND INSTUMENTS, CONTROL THEORY AND AUTOMATION, AND SPACE EXPLORATION, 2008, 7129
  • [49] No-reference assessment of blur and noise impacts on image quality
    Cohen, Erez
    Yitzhaky, Yitzhak
    SIGNAL IMAGE AND VIDEO PROCESSING, 2010, 4 (03) : 289 - 302
  • [50] Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
    Zhai, Jucai
    Zeng, Pengcheng
    Ma, Chihao
    Chen, Jie
    Zhao, Yong
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 3, 2023, : 3384 - 3392