Example-based super-resolution for enhancing spatial resolution of medical images.

This paper proposes an effective exampled-based super-resolution (SR) method to improve the spatial resolution of medical image heavily corrupted by noise. Based on the sparsity of patches, the reconstruction of a high-resolution (HR) patch from each low-resolution (LR) input patch can be performed with the help of a database, by solving a non-negative sparse optimization problem. The challenge is to effectively solve this problem in case of a large size database.

Novel example-based method for super-resolution and denoising of medical images.

In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch.

Projective analysis of 2-D images.

Recently, the use of the heatlike equation was extended to the projective case in order to find a projective analysis of curves and images; unfortunately, this formulation leads to a fifth-order partial differential equation (PDE) that is not easy to implement. Thanks to the use of a three-dimensional (3-D)homogeneous representation of a picture, we present here an alternative. Roughly speaking, it is a kind of decomposition of the heatlike formulation with well-posed second-order PDE's.