Large-scale numerical simulations are capable of generating data up to terabytes or even petabytes. As a promising method of data reduction, super-resolution (SR) has been widely studied in the scientific visualization community. However, most of them are based on deep convolutional neural networks (CNNs) or generative adversarial networks (GANs) and the scale factor needs to be determined before constructing the network. As a result, a single training session only supports a fixed factor and has poor generalization ability. To address these problems, this paper proposes a Feature-Enhanced Implicit Neural Representation (FFEINR) for spatio-temporal super-resolution of flow field data. It can take full advantage of the implicit neural representation in terms of model structure and sampling resolution. The neural representation is based on a fully connected network with periodic activation functions, which enables us to obtain lightweight models. The learned continuous representation can decode the low-resolution flow field input data to arbitrary spatial and temporal resolutions, allowing for flexible upsampling. The training process of FFEINR is facilitated by introducing feature enhancements for the input layer, which complements the contextual information of the flow field. To demonstrate the effectiveness of the proposed method, a series of experiments are conducted on different datasets by setting different hyperparameters. The results show that FFEINR achieves significantly better results than the trilinear interpolation method.
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
School of Computation, Information and Technology, and the School of Medicine and Health（计算信息学院及医学健康学院）；Technical University of Munich（慕尼黑技术大学）；Department of Computing（计算系）；Institut de Neurosciences de la Timone, UMR 7289, CNRS, Aix-Marseille Université（神经科学研究所，UMR 7289，CNRS，艾克斯-马赛大学）；Aix-Marseille Univ, APHM, Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital de la Timone（艾克斯-马赛大学，APHM，诊断与介入神经放射科，泰米翁医院）；Aix-Marseille Univ, APHM, service de neurologie pédiatrique, Hôpital de la Timone（艾克斯-马赛大学，APHM，儿童神经科，泰米翁医院）
Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events
Institute of Information Science, Beijing Jiaotong University（信息科学学院，北京交通大学）；Beijing Key Laboratory of Advanced Information Science and Network Technology（先进信息科学与网络技术北京市重点实验室）；Hefei University of Technology（合肥工业大学）；Shandong University（山东大学）
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University, Shanghai, China（上海科技大学信息科学与技术学院）；iHuman Institute, Shanghaitech University, Shanghai, China（上海科技大学iHuman研究所）
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
University of Georgia（佐治亚大学）；Stanford University（斯坦福大学）；Alphabet Inc.（Alphabet公司）；University of British Columbia（不列颠哥伦比亚大学）；University of Connecticut（康涅狄格大学）
Interlaced dynamic XCT reconstruction with spatio-temporal implicit neural representations
Nuclear Science and Engineering, Massachusetts Institute of Technology（麻省理工学院核科学与工程系）；École Polytechnique de Bruxelles, Université Libre de Bruxelles（布鲁塞尔联邦理工学院）；Electrical Engineering and Computer Science, Massachusetts Institute of Technology（麻省理工学院电气工程与计算机科学系）
Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a challenging and ill-posed problem common to industrial and medical settings. Existing 4D-CT reconstructions are designed for sparse sampling schemes that require fast CT scanners to capture multiple, rapid revolutions around the scene in order to generate high quality results. However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities. In this work, we design a reconstruction pipeline using implicit neural representatio…
Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Millenium Institute for Intelligent Healthcare Engineering（千禧智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
University of Twente（特文特大学）；Amsterdam UMC（阿姆斯特丹大学医学中心）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究所）
Abdominal aortic aneurysms (AAAs) are progressive dilatations of the abdominal aorta that, if left untreated, can rupture with lethal consequences. Imaging-based patient monitoring is required to select patients eligible for surgical repair. In this work, we present a model based on implicit neural representations (INRs) to model AAA progression. We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time. We optimize this INR using automatically extracted segmentation masks in longitudinal …
Nanjing University（南京大学）；New York University（纽约大学）
Over the past two decades, traditional block-based video coding has made remarkable progress and spawned a series of well-known standards such as MPEG-4, H.264/AVC and H.265/HEVC. On the other hand, deep neural networks (DNNs) have shown their powerful capacity for visual content understanding, feature extraction and compact representation. Some previous works have explored the learnt video coding algorithms in an end-to-end manner, which show the great potential compared with traditional methods. In this paper, we propose an end-to-end deep neural video coding framework (NVC), which uses vari…
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Information Science and Technology, ShanghaiTech University（上海交通大学信息科学与技术学院）；School of Biomedical Engineering and the National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERCAMRT), Shanghai Jiao Tong University（上海交通大学生物医学工程学院和先进磁共振诊断与治疗国家工程研究中心）
PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右岸医院（慕尼黑工业大学附属））；Imperial College London（帝国理工学院）；Pontificia Universidad Católica de Chile（智利天主教大学）；King’s College London（伦敦国王学院）
