Automatic regularization for tomographic image reconstruction

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...

A Hybrid Regularization Algorithm for High Contrast Tomographic Image Reconstruction

The common Level set based reconstruction method (LSRM) is applied to solve a piecewise constant inverse problem using one level set function and considers two different conductivity quantities for the background and the inclusion (two phases inclusion). The more the number of the piecewise constant conductivities in the medium, the higher the calculation effort of the LSRM, using multiple leve...

Tomographic Image Reconstruction

Nonlinear Back Projection for Tomographic Image Reconstruction

The objective of this paper is to investigate a new non-iterative paradigm for image reconstruction based on the use of nonlinear back projection filters. This method, which we call nonlinear back projection (NBP), attempts to directly model the optimal inverse operator through off-line training. Potential advantages of the NBP method include the ability to better account for effects of limited...

Approximate Newton’s method for Tomographic Image Reconstruction

In this report we solved a regularized maximum likelihood (ML) image reconstruction problem (with Poisson noise). We considered the gradient descent method, exact Newton’s method, pre-conditioned conjugate gradient (CG) method, and an approximate Newton’s method, with which we can solve a million variable problem. However, the current version of approximate Newton’s method converges slow. We co...