Traditional Filtering Methods such as Mean Filtering

Traditional filtering methods such as mean filtering, median filtering, and Wiener filtering are commonly employed techniques for image denoising. Mean filtering operates by calculating the average value of neighboring pixels to smooth the image, typically implemented using a sliding window convolution with uniform weights. Median filtering removes noise by replacing each pixel with the median value of its neighborhood, effectively eliminating salt-and-pepper noise through nonlinear pixel ranking operations. Wiener filtering optimizes denoising performance by leveraging statistical characteristics of the image, implementing frequency-domain restoration using power spectrum estimation techniques. Beyond traditional methods, adaptive median filtering dynamically adjusts filter size based on differences between a pixel's intensity value and its neighborhood grayscale values, providing enhanced noise removal through conditional window expansion algorithms. Therefore, selecting appropriate filtering methods is crucial in image processing for effective noise suppression and image quality enhancement, with implementation considerations including kernel size selection, boundary handling, and computational efficiency optimization.