Sampling Non-Repeating Data Points from Multiple Populations with Image Processing Implementation

This document provides four core image analysis implementations using MATLAB. The implementations cover Gaussian diffusion functions, linear diffusion, linear complex diffusion, and nonlinear diffusion algorithms. Each section includes practical code examples that demonstrate how to apply these diffusion techniques for advanced image processing and analysis. The Gaussian diffusion implementation utilizes MATLAB's fspecial() function to create Gaussian filters, while the linear diffusion codes employ finite difference methods for stable image smoothing. The linear complex diffusion module demonstrates phase preservation capabilities through complex-valued processing, and the nonlinear diffusion implementation features edge-preserving smoothing using Perona-Malik based approaches. These ready-to-use codes enable researchers to perform sophisticated image analysis and understand the practical application of diffusion-based processing techniques.