Complex PDE image denoising based on Particle Swarm Optimization
Saeid Fazli, Hamed Bouzari, Hamed Moradi Pour · 2010
Removing noise from data is often the first step in data analysis. High performance image denoising algorithms have no blurring effect on the image and no changes or relocation on the image edges. This paper presents a new approach for image denoising based on Partial Differential Equations (PDE) using Artificial Intelligence (AI) techniques. The Nonlinear Diffusion techniques and PDE-based variational models are very popular in image restoring and processing but in this proposed heuristic method, Particle Swarm Optimization (PSO) is used for Complex PDE parameter tuning by minimizing the Structural SIMilarity (SSIM) measure. Complex diffusion is a generalization of diffusion and free Schrodinger equations which has properties of both forward and inverse diffusion. The proposed method is confirmed by obtained simulation results of standard images.