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Spatial Filtering:

Spatial filtering is a fundamental image processing technique that modifies pixel values based on the characteristics of their surrounding neighborhood. It is used in a broad spectrum of applications, making a solid understanding of its principles important. This technique directly operates on the pixels of an image using a mask, often of an odd size to ensure a defined center pixel. The mask traverses all image pixels, and a predefined operation is performed within the neighborhood defined by the mask to generate a new pixel value.


Smoothing Spatial Filters

Smoothing spatial filters are used for blurring and noise reduction in images. Blurring serves as a pre-processing step to remove small details, while noise reduction is achieved through blurring using either linear or non-linear filters.
Types of smoothing filters include mean filters (linear) and order statistics filters (non-linear). A mean filter averages the pixels within the filter mask's neighborhood, replacing each pixel's value with the average of the gray levels in its neighborhood. Order statistics filters, such as median, minimum, and maximum filters, are based on ordering the pixels within the filter area and replacing the center pixel's value based on the ranking results. For instance, a median filter replaces each pixel with the median value of its neighboring pixels and is effective at removing noise.

You may refer the following books to practice more numerical questions:

1. R.C.Gonzalez and R.E.Woods, “Digital Image Processing”, Prentice Hall, 3rd Edition,2011.

2. S. Sridhar , “Digital Image Processing”, Oxford University Press,2011

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