Until recently, the most widely used methods for image reconstruction were direct analytic techniques. Iterative techniques, although computationally much more intensive, produce improved images (principally arising from more accurate modeling of the acquired projection data), enabling these techniques to replace analytic techniques not only in research settings but also in the clinic. This art...

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the...

This paper describes a fast algorithm for nonuniform image reconstruction. A multiresolution approach is formulated to compute a C2-continuous surface through a set of irregularly spaced samples. The algorithm makes use of a coarse-to-fine hierarchy of control lattices to generate a sequence of surfaces whose sum approaches the desired interpolating surface. Experimental results demonstrate tha...

Image modeling using Gibbs priors was previously shown, based on experiments, to be e1ective in image reconstruction problems. This motivated us to evaluate three methods for estimating the priors. Two of them accurately recover the parameters of the priors; however, all of them are useful for binary tomography. This is demonstrated by two sets of experiments: in one the images are from a Gibbs...

The paper is focused on the problem of estimating 3D structure and motion of nonrigid object from a monocular video sequence. Many previous methods on this problem utilize the extension technique of factorization based on rank constraint to the tracking matrix, where the 3D shape of nonrigid object is expressed as weighted combination of a set of shape bases. All these solutions are based on th...

The frequent presence of clouds in optical remotely sensed imagery prevents space and time continuity and limits its exploitation. The aim of this study is to propose a new statistical processing approach for the reconstruction of areas covered by clouds in a time sequence of optical satellite images. The approach is an iterative SOM based algorithm and is applied here to reconstruct ocean colo...

Magnetic induction tomography is a tomographic technique with potential medical applications. However, the realization of this technique remains a challenging topic both in hardware and in image reconstruction. This paper presents a hybrid image reconstruction algorithm for the image reconstruction using experimental data.

A challenging problem in security is the screening of cargo. The greater size, density, and complexity of cargo makes CT-based sensing especially diffi cult. These problems are compounded when sensing geometry is limited. In this project, we aim to develop accurate physics-based models of X-ray cargo sensing and corresponding model-based image reconstruction methods for limited angle measuremen...

The structure completion problem in fiber diffraction is addressed from a Bayesian perspective. The experimental data are sums of the squares of the amplitudes of particular sets of Fourier coefficients of the electron density. In addition, a part of the electron density is known. The image reconstruction problem is to estimate the missing part of the electron density. A Bayesian approach is ta...

With the development of ultra-high-resolution display devices, the visual perception of fine texture details is becoming increasingly important. Traditional image upsampling methods suffer from either loss of highfrequency texture details or very high time cost. In this paper, we propose an iterative projection reconstruction (IPR) method for fast and efficient image upsampling. The proposed me...