Image reconstruction by domain transform manifold learning [PDF]
Nature, 2017 Image reconstruction plays a critical role in the implementation of all contemporary imaging modalities across the physical and life sciences including optical, MRI, CT, PET, and radio astronomy. During an image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an ...
Bo Zhu +3 more
arxiv +2 more sources
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [PDF]
arXiv, 2017 Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process.
Jo Schlemper +4 more
arxiv +3 more sources
Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction [PDF]
HeliyonBackground: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction.
Varin Jaruvongvanich +10 more
doaj +2 more sources
RealFusion 360° Reconstruction of Any Object from a Single Image [PDF]
Computer Vision and Pattern Recognition, 2023 We consider the problem of reconstructing a full 360° photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed.
Luke Melas-Kyriazi +3 more
semanticscholar +1 more source
Review of Super-Resolution Image Reconstruction Algorithms [PDF]
Jisuanji kexue yu tansuo, 2022 In human visual perception system, high-resolution (HR) image is an important medium to clearly express its spatial structure, detailed features, edge texture and other information, and it has a very wide range of practical value in medicine, criminal ...
ZHONG Mengyuan, JIANG Lin
doaj +1 more source
Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022 We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.
Yutong Xie, Quanzheng Li
semanticscholar +1 more source
Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
doaj +1 more source
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [PDF]
Computer Vision and Pattern Recognition, 2021 Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction.
Pengfei Guo +4 more
semanticscholar +1 more source
NeRP: Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses additional challenges due to limited measurements. In this work, we propose a methodology of
Liyue Shen, J. Pauly, Lei Xing
semanticscholar +1 more source
Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.
Radiology, 2023 Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications.
L. Koetzier +8 more
semanticscholar +1 more source