Abstract
We present the implementation and use of filters based on masks and on statistical functions. All filters here considered operate on the image domain of finite images, so special care is taken to present actual implementations of practical algorithms. A generic convolution filter is implemented, and many instances of this kind of filters are shown: low-pass (mean, binomial and Gaussian) and high-pass filters (Laplacian) are applied to a test image which presents flat areas along with small details. A function for producing masks with arbitrary functions of the coordinates is provided, and then applied to building Gaussian masks. The relationship between blurring and variance in Gaussian masks is discussed and illustrated by examples. Image enhancement by unsharp masking is also discussed. The effect of filters is assessed by means of the resulting image and by the analysis of a profile. The minimum, median, and maximum filters are presented, along with a summary of the theoretical properties of order statistics.
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References
Arias-Castro, E., & Donoho, D. L. (2009). Does median filtering truly preserve edges better than linear filtering? Annals of Statistics, 37(3), 1172–1206.
Barrett, H. H., & Myers, K. J. (2004). Foundations of image science. Wiley-Interscience, NJ: Pure and Applied Optics.
Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. MA: Addison-Wesley.
Goudail, F., & Réfrégier, P. (2003). Statistical image processing techiques for noisy images: an application-oriented approach. Kluwer: New York.
Huber, P. J. (1981). Robust statistics. New York: Wiley.
Jain, A. K. (1989). Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice-Hall International Editions.
Lim, J. S. (1989). Two-dimensional signal and image processing: prentice hall signal processing series. Prentice Hall: Englewood Cliffs.
Lira Chávez, J. (2010). Tratamiento digital de imágenes multiespectrales (2 nd ed.). Universidad Nacional Autónoma de México. URL http://www.lulu.com..
Lopes, A., Touzi, R., & Nezry, E. (1990). Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6), 992–1000.
Maronna, R. A., Martin, R. D., & Yohai, V. J. (2006). Robust statistics: theory and methods. Wiley, England: Wiley series in Probability and Statistics.
Medeiros, M. D., Gonçalves, L. M. G. & Frery, A. C. (2010). Using fuzzy logic to enhance stereo matching in multiresolution images. Sensors, 10(2), 1093–1118. URL http://www.mdpi.com/1424-8220/10/2/1093, (Special issue: Instrumentation, Signal Treatment and Uncertainty Estimation in Sensors).
Myler, H. R., & Weeks, A. R. (1993). The pocket handbook of image processing algorithms in C. Prentice Hall: Englewood Cliffs NJ.
Russ, J. C. (1998). The image processing handbook (3rd ed.). CRC Press: USA.
Shih, F. Y. (2009). Image processing and mathematical morphology: fundamentals and applications. CRC Press: USA.
Velho, L., Frery, A. C., & Miranda, J. (2008). Image processing for computer graphics and vision (2nd ed.). London: Springer.
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© 2013 Alejandro C. Frery
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Frery, A.C., Perciano, T. (2013). Filters in the Image Domain. In: Introduction to Image Processing Using R. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-4950-7_5
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DOI: https://doi.org/10.1007/978-1-4471-4950-7_5
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