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 …

Image reconstruction in low-count PET is particularly challenging because gammas from
natural radioactivity in Lu-based crystals cause high random fractions that lower the …

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

Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss
function in an unsupervised way, is becoming very pervasive, resulting from keeping the …

Sparse representation (SR), convolutional sparse representation (CSR) and convolutional
dictionary learning (CDL) are synthetic-based priors that have proven to be successful in …

A major challenge in X-ray computed tomography (CT) is reducing radiation dose while
maintaining high quality of reconstructed images. To reduce the radiation dose, one can …

Inspired by the recent success of deep neural networks and the recent efforts to develop
multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) …

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 …

Convolutional analysis operator learning, which takes advantage of the ability to extract and
store overlapping blocks across training signals, has been the subject of much research in …