Blind image deconvolution using specified HPF for feature extraction and conjugate gradient method in frequency domain

Takanori Fujisawa, Masaaki Ikehara

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Image deconvolution is the task to recover the image information that was lost by taking photos with blur motion. Especially blind image deconvolution requires no prior informations other than the blurred image. This problem is seriously ill-posed and an additional operation is required such as extracting image features. In this paper, we present a blind image deconvolution framework using a specified highpass filter (HPF) for feature extraction to estimate a blur kernel. This problem can consider the kernel estimation in the region where salient edges are not present and improve the quality of the estimated kernel. Our approach also accelerates the deconvolution process by utilizing a conjugate gradient method in a frequency domain. This process eliminates costly convolution operations from the iterative updating and reduces the calculation time. Evaluation for 20 test images shows our framework not only performs faster than conventional frameworks but also improves the quality of recovered images.

    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
    PublisherIEEE Computer Society
    Pages2713-2717
    Number of pages5
    ISBN (Electronic)9781467399616
    DOIs
    Publication statusPublished - 2016 Aug 3
    Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
    Duration: 2016 Sep 252016 Sep 28

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    Volume2016-August
    ISSN (Print)1522-4880

    Other

    Other23rd IEEE International Conference on Image Processing, ICIP 2016
    CountryUnited States
    CityPhoenix
    Period16/9/2516/9/28

    Keywords

    • Deblurring
    • Feature Extraction
    • Optimization

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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