Denoising of medical images using a reconstruction-average mechanism

Abstract

A novel approach for denoising medical images is proposed based on a reconstruction-average mechanism. First, different parts of the original complete spectrum are chosen, from each of which a signal is reconstructed using a singularity function analysis (SFA) model. We finally achieve denoising by averaging these reconstructed signals using the fact that each of them is the sum of the same noise-free signal and an additive noise of varying magnitude. The theoretical ground of such approach is mathematically formulated. The experimental results on both simulated and real monochrome images show that the proposed denoising method allows efficient denoising while maintaining image quality, and presents significant advantages over conventional denoising methods.