Image Denoising with Non-Convex Models: A Comparison Between BDCA and nmBDCA

Data acquisition and analysis are important areas for science, directly related to image reconstruction. Much acquired data can be corrupted by various factors, such as external noise sources or those inherent to the application, but can be treated mathematically. This work aims to reconstruct images corrupted by Gaussian and Rician noise, using DC programming and a non-convex version of the total variation (TV) model. The tests are performed with a variation of BDCA (smoothing of the first DC component) and nmBDCA algorithms. The obtained results are evaluated both in quality (PSNR and SSIM) and in CPU time, covering medical computed tomography (CT) images and magnetic resonance images (MRI).