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Smoothing noisy images without destroying predefined feature carriers

  • Poster Session I
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Computer Analysis of Images and Patterns (CAIP 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1296))

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Abstract

We address the problem of smoothing gray-level images without destroying feature carriers. Smoothing is performed to suppress high, spatial-frequency noise in the image, whose relevant features contain high spatial-frequency components. The separation is obtained by using a heuristical image-surface geometry criterion over 5x5 mask. Pixel classification results with bit-fields associated with image processing tasks such as noise suppression, edge and/or some 2D-features extraction. We demonstrate the results on standard benchmark image disturbed by uncorrelated gaussian noise. Peformance of some filters applied to feature-less domains of the image is compared.

This work was supported in part by Scientific Research Committee (Poland) under grant KBN-8T11A00510, and in part by the Ministry of Education and Science (Spain) under grant SAB95-0358

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Author information

Authors and Affiliations

  1. Katedra Automatyki, Robotyki i Informatyki, Politechnika Poznanska, 60965, Poznan, Poland

    Andrzej J. Kasinski

  2. Grupo Vision y Robotica, Universidad de Murcia, 30203, Cartagena,Paseo Alfonso XIII, Spain

    Andrzej J. Kasinski

Authors
  1. Andrzej J. Kasinski
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Editor information

Gerald Sommer Kostas Daniilidis Josef Pauli

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© 1997 Springer-Verlag Berlin Heidelberg

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Kasinski, A.J. (1997). Smoothing noisy images without destroying predefined feature carriers. In: Sommer, G., Daniilidis, K., Pauli, J. (eds) Computer Analysis of Images and Patterns. CAIP 1997. Lecture Notes in Computer Science, vol 1296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63460-6_158

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  • DOI: https://doi.org/10.1007/3-540-63460-6_158

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63460-7

  • Online ISBN: 978-3-540-69556-1

  • eBook Packages: Springer Book Archive

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