Improving Particle Segmentation from Process Images with Wiener Filtering

  • Lauri Laaksonen
  • Nataliya Strokina
  • Tuomas Eerola
  • Lasse Lensu
  • Heikki Kälviäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


While there is growing interest in in-line measurements of paper making processes, the factory environment often restricts the acquisition of images. The in-line imaging of pulp suspension is often difficult due to constraints to camera and light positioning, resulting in images with uneven illumination and motion blur. This article presents an algorithm for segmenting fibers from suspension images and studies the performance of Wiener filtering in improving the sub-optimal images. Methods are presented for estimating the point spread function and noise-to-signal ratio for constructing the Wiener filter. It is shown that increasing the sharpness of the image improves the performance of the presented segmentation method.


pulp suspension fiber segmentation Wiener filtering machine vision image processing and analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lauri Laaksonen
    • 1
  • Nataliya Strokina
    • 1
  • Tuomas Eerola
    • 1
  • Lasse Lensu
    • 1
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Laboratory (MVPR) Department of Information TechnologyLappeenranta University of Technology (LUT)LappeenrantaFinland

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