Program Code Implementation for Neighborhood Averaging and Median Filtering in Digital Image Processing

During our digital image processing coursework, we conducted in-depth research on the program code implementations for neighborhood averaging and median filtering algorithms. Through comparative analysis of these filtering methods' strengths and limitations, we gained comprehensive understanding of their roles and applications in image processing. Our implementation involved creating kernel-based convolution functions for neighborhood averaging, which calculates the mean value of pixels within a specified window size (typically 3×3 or 5×5) to achieve image smoothing. For median filtering, we developed sorting algorithms to replace each pixel with the median value of its neighboring pixels, particularly effective for salt-and-pepper noise removal. Experimental verification demonstrated that neighborhood averaging effectively smooths images and reduces noise impact through pixel value averaging, while median filtering excels at eliminating impulse noise by preserving edge information. Our experiments concluded that selecting appropriate filtering methods is crucial in digital image processing, as different techniques yield varying results and applications depending on specific scenarios - neighborhood averaging suits general noise reduction while median filtering proves superior for impulse noise scenarios requiring edge preservation.