Robust Image Reconstruction With Misaligned Structural Information

Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying spa...

Robust Laplacian Regularization for Enhanced Image Reconstruction

This paper presents a new robust regularization approach to the reconstruction of enhanced images from noisy observations. A new regularization constraint designed explicitly to boost nonnoise fine image details is optimized together with a traditional two-term (smooth and fidelity) regularization functional. A gradient descent based numerical solution is developed which is shown to be numerica...

Robust Multiresolution Techniques for Image Reconstruction

The reconstruction of images from projections, diffraction fields, or other similar measurements requires applying signal processing techniques within a physical context. Although modeling of the acquisition procedure can conveniently be carried out in the continuous domain, actual reconstruction from experimental measurements requires the derivation of discrete algorithms that are accurate, ef...

Robust Image Reconstruction from Multiview Measurements

We propose a novel method to accurately reconstruct a set of images representing a single scene from few linear multi-view measurements. Each observed image is modeled as the sum of a background image and a foreground one. The background image is common to all observed images but undergoes geometric transformations, as the scene is observed from different viewpoints. In this paper, we assume th...

Image Reconstruction from Incomplete Data