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We develop and experimentally demonstrate an imaging method based on the pink noise pattern in the computational ghost imaging (CGI) system, which has a strong ability to photograph moving objects. To examine its unique ability and scope of…
Cardiac cine magnetic resonance imaging (MRI) can be considered the optimal criterion for measuring cardiac function. This imaging technique can provide us with detailed information about cardiac structure, tissue composition and even blood…
We have developed a 3 dimensional Coherent Diffraction Imaging (CDI) algorithm to retrieve phases of diffraction patterns of samples in Grazing Incidence Small Angle X-ray Scattering (GISAXS) experiments. The algorithm interprets the…
The Magnetic Resonance Imaging (MRI) processing chain starts with a critical acquisition stage that provides raw data for reconstruction of images for medical diagnosis. This flow usually includes a near-lossless data compression stage that…
In many applications of tomography, the acquired projections are either limited in number or contain a significant amount of noise. In these cases, standard reconstruction methods tend to produce artifacts that can make further analysis…
Temporal ghost imaging (TGI) enables ultrafast temporal signal recovery using slow detectors, offering a promising route for high-speed mid-infrared (MIR) detection. However, conventional schemes remain limited in temporal resolution by the…
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT image and a noise image from the high-dose CT image. Then,…
X-ray imaging is an invaluable tool for noninvasive analysis in many fields ranging from basic science to medicine and security. The development of low-dose large area imaging solutions still represents an important challenge for various…
High-quality global illumination (GI) in real-time rendering is commonly achieved using precomputed lighting techniques, with lightmap as the standard choice. To support GI for static objects in dynamic lighting environments, multiple…
The paper[1] presented a novel muon radiography technique which exploits the multiple Coulomb scattering of these particles for nondestructive inspection without the use of artificial radiation. In this paper, a new kind algorithm named…
Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional…
This work proposes RIS-enabled channel signature modulation (RIS-CSM), a lightweight index modulation scheme for reconfigurable intelligent surfaces (RIS). An N-element RIS is partitioned into disjoint groups, each employing predetermined…
The penetrating power of X rays underpins important applications such as medical radiography. However, this same attribute makes it challenging to achieve flexible on-demand patterning of X-ray beams. One possible path to this goal is…
A special algorithm for the Fourier-transform Ghost Imaging (GI) scheme is discussed based on the Compressive Sampling (CS) theory. The CS algorithm could also be used for the Fourier spectrum reconstruction of pure phase object by setting…
High-contrast imaging presents us with the opportunity to study circumstellar disks and the planets still embedded within them -- providing key insights into the formation and evolution of planetary systems. However, the post-processing…
Classical shadow tomography (CST) involves obtaining enough classical descriptions of an unknown state via quantum measurements to predict the outcome of a set of quantum observables. CST has numerous applications, particularly in…
Computational modeling of the brain has become a key part of understanding how the brain clears metabolic waste, but patient-specific modeling on a significant scale is still out of reach with current methods. We introduce a novel approach…
Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image…
This work is concerned with applying iterative image reconstruction, based on constrained total-variation minimization, to low-intensity X-ray CT systems that have a high sampling rate. Such systems pose a challenge for iterative image…
A wide range of diagnostic information in medicine is currently obtained using radioactive tracers. While central to nuclear medicine, these methods are inherently constrained: radiation dose limits repeat examinations, short tracer…