Nonlinear Diffusion Image Processing Toolbox

This toolbox offers an extensive collection of functions implementing nonlinear diffusion for image processing. The nonlinear diffusion technique serves as a powerful approach for noise suppression and image simplification, effectively preparing images for subsequent segmentation tasks. The implementation heavily incorporates foundational work by Perona and Malik, along with advanced methodologies from J. Weickert's research papers. Key features include efficient Additive Operator Splitting (AOS) diffusion implementation, 3D volumetric diffusion capabilities, color image diffusion processing, and Coherence-Enhancing Diffusion filters. The toolbox architecture allows users to apply various diffusion techniques through modular function calls, with parameters controlling diffusion strength and edge preservation. Each function includes optimized numerical schemes for stable computation, particularly handling gradient-based conductivity functions that preserve significant edges while smoothing homogeneous regions. This versatile toolkit enables researchers to experiment with multiple diffusion strategies and significantly improve image quality through well-documented, customizable code implementations.