Improving particle segmentation from process images with Wiener filtering

  • Authors:

  • Lauri Laaksonen;Nataliya Strokina;Tuomas Eerola;Lasse Lensu;Heikki Kälviäinen

  • Affiliations:

  • Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland;Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland;Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland;Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland;Machine Vision and Pattern Recognition Laboratory, Department of Information Technology, Lappeenranta University of Technology, Lappeenranta, Finland

  • Venue:
  • SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
  • Year:
  • 2011

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Abstract

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.