Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in
imaging, image processing, and computer vision. INNs combine regression NNs and an …

Background Advancements in tomographic medical imaging have revolutionized
diagnostics and treatment monitoring by offering detailed 3D visualization of internal …

Convolutional operator learning is gaining attention in many signal processing and
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …

Patient radiation dose associated with X-ray CT is a significant concern in the medical
community. One of the ways to reduce patient dose is to acquire projection data at fewer …

A deep learning method for laser absorption tomography was developed to effectively
integrate physical priors related to flow-field thermochemistry and transport. Mid-fidelity …

Techniques involving learned dictionaries can outperform conventional approaches
involving (nontrained) analytical sparsifying models for MR image reconstruction. Inspired …

Convolutional analysis operator learning (CAOL) methods train an autoencoding
convolutional neural network (CNN) in an unsupervised learning manner, to more …

This paper applies the recent fast iterative neural network framework, Momentum-Net, using
appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction …

Deep image mapping networks have been recently applied to solving some inverse
problems in imaging due to their good mapping capabilities. However, the greater mapping …

Increasingly more patients exposed to radiation from computed axial tomography (CT) will
have a greater risk of developing tumors or cancer that are caused by cell mutation in the …