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    Deep video deblurring

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    1611.08387v1.pdf
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    8.178Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Su, Shuochen
    Delbracio, Mauricio
    Wang, Jue
    Sapiro, Guillermo
    Heidrich, Wolfgang cc
    Wang, Oliver
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Visual Computing Center (VCC)
    Date
    2016-11-25
    Permanent link to this record
    http://hdl.handle.net/10754/626481
    
    Metadata
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    Abstract
    Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task which requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high framerate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
    Publisher
    arXiv
    arXiv
    1611.08387
    Additional Links
    http://arxiv.org/abs/1611.08387v1
    http://arxiv.org/pdf/1611.08387v1
    Collections
    Preprints; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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