Genetic Normalized Convolution

  • Giulia Albanese
  • Marco Cipolla
  • Cesare Valenti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)


Normalized convolution techniques operate on very few samples of a given digital signal and add missing information, trough spatial interpolation. From a practical viewpoint, they make use of data really available and approximate the assumed values of the missing information. The quality of the final result is generally better than that obtained by traditional filling methods as, for example, bilinear or bicubic interpolations. Usually, the position of the samples is assumed to be random and due to transmission errors of the signal. Vice versa, we want to apply normalized convolution to compress data. In this case, we need to arrange a higher density of samples in proximity of zones which contain details, with respect to less significant, uniform parts of the image. This paper describes an evolutionary approach to evaluate the position of certain samples, in order to reconstruct better images, according to a subjective definition of visual quality. An extensive analysis on real data was carried out to verify the correctness of the proposed methodology.


Genetic Algorithm Radial Symmetry Texture Scene Phase Congruency Active Vision System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Giulia Albanese
    • 1
  • Marco Cipolla
    • 2
  • Cesare Valenti
    • 2
  1. 1.Dipartimento di Scienze dell’InformazioneUniversità di BolognaItaly
  2. 2.Dipartimento di Matematica e InformaticaUniversità di PalermoItaly

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