Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models [0.03%]
基于字典模型的在线自适应图像重建（OnAIR）方法
Brian E Moore,Saiprasad Ravishankar,Raj Rao Nadakuditi et al.
Brian E Moore et al.
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image...
Tzu-An Song,Samadrita Roy Chowdhury,Fan Yang et al.
Tzu-An Song et al.
Positron emission tomography (PET) suffers from severe resolution limitations which reduce its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs)....
Multiple-Input Multiple-Output (MIMO) MRI: Combining Parallel Excitation and Parallel Reception for Enhanced Imaging [0.03%]
多输入多输出(MIMO)磁共振成像技术：结合并行激发和并行采集实现高性能成像
Xianglun Mao,Nicole L Vike,Thomas M Talavage et al.
Xianglun Mao et al.
Magnetic resonance imaging (MRI) plays a critical role in visualizing the structure and functions of the human body. In order to accelerate imaging time and improve image quality, radio-frequency (RF) coil receive arrays are commonly employ...
Efficient Dynamic Parallel MRI Reconstruction for the Low-Rank Plus Sparse Model [0.03%]
动态并行低秩稀疏模型的高效MRI重建算法研究
Claire Yilin Lin,Jeffrey A Fessler
Claire Yilin Lin
The low-rank plus sparse (L+S) decomposition model enables the reconstruction of under-sampled dynamic parallel magnetic resonance imaging (MRI) data. Solving for the low-rank and the sparse components involves non-smooth composite convex o...
PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior [0.03%]
基于联合熵先验的 PET 图像去卷积与超分辨重建方法
Tzu-An Song,Fan Yang,Samadrita Roy Chowdhury et al.
Tzu-An Song et al.
The intrinsically limited spatial resolution of PET confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution MR images. The framework ...
Edward T Reehorst,Philip Schniter
Edward T Reehorst
Regularization by Denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. Expe...
Monotone FISTA with Variable Acceleration for Compressed Sensing Magnetic Resonance Imaging [0.03%]
变加速单调FISTA在压缩感知磁共振成像中的应用研究
Marcelo V W Zibetti,Elias S Helou,Ravinder R Regatte et al.
Marcelo V W Zibetti et al.
An improvement of the monotone fast iterative shrinkage-thresholding algorithm (MFISTA) for faster convergence is proposed. Our motivation is to reduce the reconstruction time of compressed sensing problems in magnetic resonance imaging. Th...
A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields [0.03%]
含不确定平整场的X射线层析成像凸重建模型
Hari Om Aggrawal,Martin S Andersen,Sean Rose et al.
Hari Om Aggrawal et al.
Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time ...
Greg Ongie,Mathews Jacob
Greg Ongie
Fourier domain structured low-rank matrix priors are emerging as powerful alternatives to traditional image recovery methods such as total variation (TV) and wavelet regularization. These priors specify that a convolutional structured matri...
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems [0.03%]
高效外积和字典学习(SOUP-DIL)及其在逆问题中的应用
Saiprasad Ravishankar,Raj Rao Nadakuditi,Jeffrey A Fessler
Saiprasad Ravishankar
The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared t...