Super-resolution/segmentation of 3D trabecular bone images with total variation and nonconvex Cahn-Hilliard functional

The analysis of trabecular bone micro structure from in-vivo CT images is still limited due to insufficient spatial resolution. In a previous work, we have investigated the use of super resolution techniques to improve image quality based on a TV based approach. However, the method is limited to recover the bimodal nature of the image. In this work, we investigate the use of a double well non convex constraint to solve the joint super resolution/segmentation problem. Two different minimization schemes are proposed to obtain a critical point of the non convex functional. The two methods improve the reconstruction results on real data.