MATLAB Implementation of Image Smoothing Algorithms

Implementing custom image smoothing algorithms presents an engaging and challenging task in digital image processing. In this project, we explore different filtering methods to enhance image quality, focusing on two fundamental approaches: mean filtering and median filtering. Mean filtering operates by computing the average value of pixels within a specified neighborhood, effectively reducing random noise through spatial averaging. Median filtering employs a different strategy by sorting pixel values in the neighborhood and selecting the median value, making it particularly effective against salt-and-pepper noise while preserving edges better than linear filters. To achieve optimal image output results, we experiment with various mean filter kernel configurations. The kernel size directly influences the smoothing intensity - larger kernels (e.g., 5×5 or 7×7) provide broader smoothing coverage suitable for heavy noise reduction, while smaller kernels (3×3) maintain finer image details. Additionally, we can implement differently shaped kernels such as circular, rectangular, or cross-shaped templates to accommodate specific image characteristics and noise patterns. The implementation involves careful boundary handling and efficient convolution operations using MATLAB's matrix manipulation capabilities. Developing custom image smoothing algorithms provides deep insights into spatial filtering principles and their practical applications. This hands-on approach enhances understanding of trade-offs between noise removal and detail preservation, while improving programming skills through optimized algorithm implementation. The project demonstrates important concepts like convolution operations, boundary padding strategies, and computational efficiency considerations using vectorized MATLAB code. In conclusion, creating custom image smoothing algorithms represents a rewarding project that combines theoretical knowledge with practical implementation skills. Through systematic experimentation with different filter types and parameters, we can achieve superior image enhancement results while advancing our expertise in digital image processing techniques and MATLAB programming.