MATLAB Implementation of Image Median Filtering with Performance Analysis

Image median filtering is a widely used image processing technique. It operates by sorting pixel values within a neighborhood around each pixel and selecting the median value as the new pixel value. In MATLAB implementation, this typically involves using the medfilt2() function for 2D images, which processes each pixel by examining its surrounding window (commonly 3x3 or 5x5 pixels). Compared to low-pass filtering (often implemented using imfilter() with averaging or Gaussian kernels), median filtering demonstrates superior performance in removing noise while preserving image edges and details more effectively. To quantitatively compare the performance differences between median and low-pass filtering, Peak Signal-to-Noise Ratio (PSNR) analysis serves as a key evaluation metric. The PSNR calculation can be implemented using MATLAB's psnr() function, which computes the ratio between the maximum possible power of the original image and the power of distorting noise. Higher PSNR values indicate better image quality preservation. By comparing PSNR values obtained from both filtering methods, researchers can objectively assess their respective advantages and limitations in various image processing scenarios, including noise reduction effectiveness and detail preservation capabilities.