MATLAB Function Implementation: Mean Filter with Configurable Window Sizes

This MATLAB function implements mean filtering with configurable window sizes including 3×3, 5×5, and other dimensional options. Mean filtering is a fundamental image processing technique used for image smoothing and noise reduction, achieved by replacing each pixel's value with the average of pixels within a surrounding window. The core algorithm operates through these computational steps: 1. Defines a sliding window of specified dimensions (e.g., 3×3 or 5×5) 2. Computes the arithmetic mean of all pixel values within the current window 3. Replaces the central pixel value with the calculated average 4. Iterates across all image pixels using symmetric padding for border handling Key implementation features include: - Dynamic window size configuration through input parameters - Efficient matrix operations using MATLAB's conv2 or imfilter functions - Automatic boundary handling with symmetric padding to maintain image dimensions Larger window sizes (e.g., 5×5) provide stronger smoothing effects but may cause detail blurring, while smaller windows (3×3) preserve more details with moderate noise reduction. The appropriate window size should be selected based on specific application requirements balancing noise suppression and detail preservation. The function structure typically includes: - Input validation for window size parameters - Kernel normalization to ensure proper averaging - Optional parameter for handling different image color channels - Output verification for data type consistency with input image