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    Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction

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    Type
    Conference Paper
    Authors
    Sun, Tiancheng
    Peng, Yifan
    Heidrich, Wolfgang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Visual Computing Center (VCC)
    Date
    2017-12-25
    Online Publication Date
    2017-12-25
    Print Publication Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/627152
    
    Metadata
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    Abstract
    Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules. In simplified optical designs, chromatic aberration can be one of the most significant causes for degraded image quality, and it can be quite difficult to remove in post-processing, since it results in strong blurs in at least some of the color channels. In this work, we revisit the pixel-wise similarity between different color channels of the image and accordingly propose a novel algorithm for correcting chromatic aberration based on this cross-channel correlation. In contrast to recent weak prior-based models, ours uses strong pixel-wise fitting and transfer, which lead to significant quality improvements for large chromatic aberrations. Experimental results on both synthetic and real world images captured by different optical systems demonstrate that the chromatic aberration can be significantly reduced using our approach.
    Citation
    Sun T, Peng Y, Heidrich W (2017) Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction. 2017 IEEE International Conference on Computer Vision (ICCV). Available: http://dx.doi.org/10.1109/ICCV.2017.352.
    Sponsors
    This work was supported by KAUST baseline funding, as well as a UBC 4YF Doctoral Fellowship. The authors thank Tao Yue, Qiang Fu, and Felix Heide for the help on synthetic results.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE International Conference on Computer Vision (ICCV)
    Conference/Event name
    16th IEEE International Conference on Computer Vision, ICCV 2017
    DOI
    10.1109/ICCV.2017.352
    Additional Links
    http://ieeexplore.ieee.org/document/8237614/
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2017.352
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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