Smooth Noise Reduction for 3D Images using Perona-Malik Anisotropic Diffusion Model

This paper presents a novel approach for addressing noise issues in 3D images using the Perona-Malik anisotropic diffusion model. The implementation involves solving a partial differential equation where diffusion strength is controlled by the image gradient magnitude, allowing selective smoothing that preserves important edge information. The algorithm employs a diffusion function typically implemented as g(||∇I||) = 1/(1 + (||∇I||/K)^2), where K is a contrast parameter that determines the sensitivity to edges. Through iterative updates using finite difference methods, the model effectively reduces Gaussian noise while maintaining structural integrity. This method proves particularly valuable in 3D image processing applications, including medical imaging analysis and computer vision systems, where edge preservation is crucial for accurate interpretation. The model's ability to differentiate between noise and meaningful features makes it a powerful tool for future 3D image enhancement workflows.