Variational Models for Simultaneous Image Segmentation and Noise Removal
Author | : Iulia Magdalena Posirca |
Publisher | : |
Total Pages | : 54 |
Release | : 2012 |
Genre | : |
ISBN | : |
We present two projects for simultaneous image segmentation and noise removal. The first project concerns the images corrupted with Gaussian noise and the second one was developed for images contaminated with multiplicative noise. For both models we use soft segmentation, which allows each pixel to belong to each image pattern with some probability. Our work proposes also a functional with variable exponent, which provides a better noise removal with feature preserving. The diffusion resulting from the proposed models is a combination between the total variation (TV)-based and isotropic smoothing. To minimize the functional energy, we use the Euler-Lagrange equations on the (K-1)-simplex and the alternating minimization (AM) algorithm. The experimental and comparison results with some traditional models show the efficiency of our work, with improved denoising and segmentation of real and synthetic images.