Image Filtering and Denoising with Median Filter Implementation

Median filtering for image denoising represents a widely used image processing technique. The median filter effectively smooths images while efficiently removing various types of noise through its nonlinear filtering approach that replaces each pixel's value with the median of neighboring pixels. Beyond median filtering, several alternative denoising methods are available, including mean filtering (using averaging kernels) and Gaussian filtering (employing Gaussian convolution kernels). To better evaluate denoising performance, various noise types such as Gaussian noise or salt-and-pepper noise can be added to images using functions like "imnoise" in MATLAB. By comparing the original image with the median-filtered result using side-by-side visualization or quantitative metrics like PSNR, the effectiveness of median filtering becomes clearly apparent. The implementation typically involves using MATLAB's "medfilt2" function for 2D median filtering with configurable neighborhood sizes. In summary, median filter denoising serves as a straightforward yet powerful image processing method that enhances image quality while preserving critical edge information and important image features through its noise-reduction capabilities.