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.
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Allied Vision Technologies. Guppy Techical Manual (2009)
Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Modeling and Simulation 4, 490–530 (2005)
Canny, J.: A computational approach to edge detection. IEEE Transactions of Pattern Analysis and Machine Intelligence 8, 679–698 (1986)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)
Gonzales, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, London (2008)
Lilliefors, H.W.: On the kolmogorov-smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association 62, 399–402 (1967)
Murphy, B.W., Carson, P.L., Ellis, J.H., Zhang, Y.T., Hyde, R.J., Chenevert, T.L.: Signal-to-noise measures for magnetic resonance imagers. Magnetic Resonance Imaging 2, 425–428 (1993)
Szeliski, R., Joshi, N., Kriegman, D.J.: Psf estimation using sharp edge prediction. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Pang, M.-C.: A novel blind super-resolution technique based on the improved poisson maximum a posteriori algorithm. International Journal of Imaging Systems and Technology 12, 239–246 (2002)
Robertson, G., Olson, J., Allen, P., Chan, B., Seth, R.: Measurement of fiber length, coarseness, and shape with the fiber quality analyzer. Tappi Journal 82, 93–98 (1999)
Saarela, J., Törmänen, M., Myllylä, R.: Measuring pulp consistency and fines content with a streak camera. Measurement Science and Technology 14, 1801–1806 (2003)
Sitholé, B., Filion, D.: Assessment of methods for the measurement of macrostickies in recycled pulps. Progress in Paper Recycling 17 (2008)
Wang, F., Hubbe, M.: Development and evaluation of an automated streaming potential measurement device. Colloids and Surfaces A: Physicochemical and Engineering Aspects 194, 221–232 (2001)
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Laaksonen, L., Strokina, N., Eerola, T., Lensu, L., Kälviäinen, H. (2011). Improving Particle Segmentation from Process Images with Wiener Filtering. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_27
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DOI: https://doi.org/10.1007/978-3-642-21227-7_27
Publisher Name: Springer, Berlin, Heidelberg
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