Momentum-Net: Fast and convergent iterative neural network for inverse problems
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 …
imaging, image processing, and computer vision. INNs combine regression NNs and an …
Improved low-count quantitative PET reconstruction with an iterative neural network
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 …
natural radioactivity in Lu-based crystals cause high random fractions that lower the …
Convolutional analysis operator learning: Acceleration and convergence
Convolutional operator learning is gaining attention in many signal processing and
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …
computer vision applications. Learning kernels has mostly relied on so-called patch-domain …
Accelerated Log-Regularized Convolutional Transform Learning and Its Convergence Guarantee
Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss
function in an unsupervised way, is becoming very pervasive, resulting from kee** the …
function in an unsupervised way, is becoming very pervasive, resulting from kee** the …
Convolutional analysis operator learning for multifocus image fusion
C Zhang, Z Feng - Signal Processing: Image Communication, 2022 - Elsevier
Sparse representation (SR), convolutional sparse representation (CSR) and convolutional
dictionary learning (CDL) are synthetic-based priors that have proven to be successful in …
dictionary learning (CDL) are synthetic-based priors that have proven to be successful in …
Learning deep analysis dictionaries for image super-resolution
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) …
multi-layer dictionary models, we propose a Deep Analysis dictionary Model (DeepAM) …
Sparse-View X-Ray CT Reconstruction Using Prior with Learned Transform
X Zheng, IY Chun, Z Li, Y Long, JA Fessler - ar** blocks across training signals, has been the subject of much research in …