Spatially Adapted First and Second Order Regularization for Image Reconstruction: From an Image Surface Perspective

Image Reconstruction from Incomplete Data

Surface reconstruction of detect contours for medical image registration purpose

Although, most of the abnormal structures of human brain do not alter the shape of outer envelope of brain (surface), some abnormalities can deform the surface extensively. However, this may be a major problem in a surface-based registration technique, since two nearly identical surfaces are required for surface fitting process. A type of verification known as the circularity check for th...

Image Reconstruction from Incomplete Data

Direct high-order edge-preserving regularization for tomographic image reconstruction

In this paper we present a new two-level iterative algorithm for tomographic image reconstruction. The algorithm uses a regularization technique, which we call edge-preserving Laplacian, that preserves sharp edges between objects while damping spurious oscillations in the areas where the reconstructed image is smooth. Our numerical simulations demonstrate that the proposed method outperforms to...

Tensor Based Second Order Variational Model for Image Reconstruction

Second order total variation (SOTV) models have advantages for image reconstruction over their first order counterparts including their ability to remove the staircase artefact in the reconstructed image, but they tend to blur the reconstructed image. To overcome this drawback, we introduce a new Tensor Weighted Second Order (TWSO) model for image reconstruction. Specifically, we develop a nove...