MATLAB Code Implementation for Impulse Noise Removal

Impulse noise is a common type of noise in digital image processing, characterized by randomly occurring high-intensity black and white pixels in images. To effectively remove this noise, various filtering methods can be compared and analyzed.

Median Filtering Median filtering is a nonlinear filtering technique that replaces the center pixel value with the median value of neighboring pixels. This method is particularly effective for removing impulse noise because it suppresses isolated noise points without significantly blurring image details. In MATLAB implementation, the medfilt2() function can be applied with different kernel sizes (e.g., 3×3 or 5×5 windows) to process grayscale images.

Mean Filtering Mean filtering is a linear filtering approach that uses a sliding window to compute the average value of neighboring pixels to smooth out noise. Although computationally simple, its effectiveness in suppressing impulse noise is inferior to median filtering, and it tends to cause image blurring. MATLAB's imfilter() or fspecial() functions with 'average' option can implement this method, where the filter size parameter controls the degree of smoothing.

PCNN Filtering (Pulse-Coupled Neural Network) PCNN is an image processing method based on biological neural networks that removes noise by simulating pulse synchronization characteristics between neurons. PCNN filtering can adaptively handle different types of noise, including impulse noise, while preserving image edges and details effectively. Implementation typically involves custom MATLAB code defining neuron linking, pulse firing thresholds, and iterative processing cycles.

Signal-to-Noise Ratio (SNR) Calculation To evaluate the denoising performance of different filtering methods, the Signal-to-Noise Ratio (SNR) of the processed image can be calculated. SNR reflects the ratio between the signal (original image) and noise components, with higher values indicating better denoising results. MATLAB implementation involves using functions like snr() or custom calculations comparing pixel intensity variances between original and processed images.

In MATLAB, the implementation typically follows these steps: First, read or generate an image contaminated with impulse noise using imnoise() function with 'salt & pepper' parameter. Apply denoising processing using median filtering (medfilt2()), mean filtering (imfilter()), and PCNN filtering (custom implementation). Calculate the SNR values between denoised images and the original noise-free image to compare method effectiveness.

By comparing SNR values of these three methods, one can intuitively determine which filtering algorithm works best for the current noise type. Typically, median filtering performs well for impulse noise removal, while PCNN filtering shows advantages in complex noise environments with better edge preservation capabilities.