We propose a novel iterative segmentation algorithm (iS Sy. Da. TA: Iterative Segmentation
Synthetic Data Training Algorithm) employing Deep Convolutional Neural Networks and …

Imaging quality is a critical component of compressive imaging in real applications. In this
study, we propose a compressive imaging method based on multi-scale modulation and …

For nearly twenty years, a multitude of Compressive Imaging (CI) techniques have been
under development. Modern approaches to CI leverage the capabilities of Deep Learning …

This work presents a framework to exploit the synergy between Digital Volume Correlation
(DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X …

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications
in order to reduce the scanning cost and improve the patient experience. This can also …

Micro Computed Tomography (μCT) can be applied for three-dimensional characterization
of structural features like pores in a non-destructive way. The resolution of the volumetric …

The real-time imaging is important in automatic successive inspection with micro-
computerized tomography (micro-CT). Generally, the size of the detector is chosen …

Purpose Sparsely sampled computed tomography (CT) has been attracting attention as a
technique that can reduce the high radiation dose of conventional CT. In general, iterative …

Compressive sensing (CS) has proven to be an efficient technique for accelerating magnetic
resonance imaging (MRI) acquisition through breaking the Nyquist sampling limit. However …

The problem of reconstructing an image from a set of tomographic data is not new, nor is it
lacking attention. However there is still a distinct gap between the mathematicians and the …