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Principles Of Image Deblurring

Author

Listed:
  • Alexandra Ghecenco

    ("Politehnica" University of Bucharest, 060042 Bucharest, Romania)

Abstract

This paper presents a method for deblurring distorted images using a known convolution kernel. Pre-existing methods and algorithms are presented, each with its advantages and disadvantages. In this implementation, Gaussian kernels are considered for blurring. The algorithmic approach is detailed and the sampled results show the effectiveness of the chosen method.

Suggested Citation

  • Alexandra Ghecenco, 2014. "Principles Of Image Deblurring," Romanian Economic Business Review, Romanian-American University, vol. 8(2), pages 488-497, December.
    • Handle: RePEc:rau:journl:v:8:y:2014:i:2:p:488-497
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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/WI14/JISOM-WI14-A27.pdf
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    References listed on IDEAS

    as
    1. Costin-Anton Boiangiu & Mihai Cristian Tanase & Radu Ioanitescu, 2013. "Text Line Segmentation In Handwritten Documents Based On Dynamic Weights," Romanian Economic Business Review, Romanian-American University, vol. 7(2), pages 247-254, December.
    2. Andreea-Mihaela Pintilie & Costin-Anton Boiangiu, 2013. "Study Of Neurobiological Images Based On Ontologies Used In Super-Resolution Algorithms," Romanian Economic Business Review, Romanian-American University, vol. 7(2), pages 300-308, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Costin-Anton Boiangiu & Ionut-Adrian Nitu, 2016. "Automatic Image Resampling Filter Generation," Romanian Economic Business Review, Romanian-American University, vol. 10(2), pages 487-502, December.

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