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Motion Blur Prior

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00533761" target="_blank" >RIV/67985556:_____/20:00533761 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191316" target="_blank" >http://dx.doi.org/10.1109/ICIP40778.2020.9191316</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICIP40778.2020.9191316" target="_blank" >10.1109/ICIP40778.2020.9191316</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Motion Blur Prior

  • Original language description

    We have proposed a novel methodology for generating priors that favor motion blur. Priors play an important role of regularizers in image deblurring algorithms. Image priors are frequently studied and many forms were proposed in the literature. Blur priors are considered less important and the most common forms are simple uniform distributions with domain constraints. We propose a more informative blur prior based on the notion of atomic norm which favors blurs composed of line segments and is suitable for motion blur. The prior is formulated as a linear program that can be inserted into any optimization task. Evaluation is conducted on blind deblurring of moving objects.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20206 - Computer hardware and architecture

Result continuities

  • Project

    <a href="/en/project/GA18-05360S" target="_blank" >GA18-05360S: Solving inverse problems for the analysis of fast moving objects</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2020 IEEE International Conference on Image Processing (ICIP)

  • ISBN

    978-1-7281-6396-3

  • ISSN

    1522-4880

  • e-ISSN

    2381-8549

  • Number of pages

    5

  • Pages from-to

    928-932

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    Abu Dhabi

  • Event date

    Oct 25, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article