On hallucinations in tomographic image reconstruction

Tomographic Image Reconstruction

On Sparsity Maximization in Tomographic Particle Image Reconstruction

This work focuses on tomographic image reconstruction in experimental fluid mechanics (TomoPIV), a recently established 3D particle image velocimetry technique. Corresponding 2D image sequences (projections) and the 3D reconstruction via tomographical methods provides the basis for estimating turbulent flows and related flow patterns through image processing. TomoPIV employs undersampling to ma...

Proposal of fault-tolerant tomographic image reconstruction

This paper deals with tomographic image reconstruction under the situation where some of projection data bins are contaminated with abnormal data. Such situations occur in various instances of tomography. We propose a new reconstruction algorithm called the Fault-Tolerant reconstruction outlined as follows. The least-squares (Lnorm) error function ‖ A~x−~b ‖2 used in ordinary iterative reconstr...

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...