Mean Filtering, Weighted Filtering, Median Filtering - MATLAB Examples

This text provides additional information to expand the content while maintaining the original key concepts. The following MATLAB implementation examples demonstrate practical applications in image and signal processing:

- Mean Filtering: A fundamental image processing technique that smooths images and reduces noise by replacing each pixel value with the average of its neighborhood. Implementation typically involves using MATLAB's fspecial('average', [m n]) function to create an averaging kernel and imfilter() for convolution operations.

- Weighted Filtering: An advanced filtering method that applies different weights to pixels within the neighborhood region, preserving image details more effectively than standard mean filtering. This can be implemented using Gaussian filters (fspecial('gaussian')) or custom weight matrices that emphasize central pixels while gradually reducing weights towards the edges.

- Median Filtering: A powerful noise reduction technique particularly effective against salt-and-pepper noise, which replaces each pixel with the median value of its surrounding area. MATLAB's medfilt2() function provides efficient implementation for 2D images, using sliding window operations to calculate median values while preserving edges and eliminating outliers.

These MATLAB examples with code implementation details help developers better understand and apply fundamental concepts in image and signal processing workflows.