In this paper, we propose a novel deep convolutional neural network (CNN)-based
algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have …

Tomographic image reconstruction is a computationally demanding task, even more so
when advanced models are used to describe a more complete and accurate picture of the …

The proposed paper addresses the inverse problems using a novel deep convolutional
neural network (CNN). Over the years, regularized iterative algorithms have been observed …

As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent
demand in developing countries. Under some circumstances, in order to reduce the scan …

Uncompressed clinical data from modern positron emission tomography (PET) scanners are
very large, exceeding 350 million data points (projection bins). The last decades have seen …

Sparse-view and limited-angle Computed Tomography (CT) are very challenging problems
in real applications. Due to the high ill-posedness, both analytical and iterative …

Computed Tomography (CT) Image Reconstruction is an important technique used in a
variety of domains, including medical imaging, electron microscopy, non-destructive testing …

Recent studies have shown that using fine-grained peer-to-peer (P2P) stores to
communicate among devices in multi-GPU systems is a promising path to achieve strong …

We suggest a new implementation for rapid reconstruction of three-dimensional (3-D)
refractive index (RI) maps of biological cells acquired by tomographic phase microscopy …

Target detection for hyperspectral images (HSIs) is one of the significant techniques in
remote sensing data processing. Targets generally comprise various object categories with …