Nonconvex Regularization for Image Segmentation

We propose a new method for image segmentation based on a variational regularization algorithm for image denoising. We modify the Rudin-Osher-Fatemi (ROF) model in [1] by minimizing the p L -norm of the gradient, where 0 > p is very small. The result is that we better preserve edges, while flattening regions away from the edges. This results in an automatic segmentation of the image into several regions, which does not require any prior knowledge about the number of those regions, or their intensity levels.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[3]  Thomas J. Asaki,et al.  A Variational Approach to Reconstructing Images Corrupted by Poisson Noise , 2007, Journal of Mathematical Imaging and Vision.

[4]  Curtis R. Vogel,et al.  Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..

[5]  Rick Chartrand,et al.  Nonconvex Regularization for Shape Preservation , 2007, 2007 IEEE International Conference on Image Processing.

[6]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .