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.
Frery, A. C., & Perciano, T. (2013). Filters in the image domain. In SpringerBriefs in Computer Science (Vol. 0, pp. 59–75). Springer. https://doi.org/10.1007/978-1-4471-4950-7_5