Aiming at the problems that the convolutional neural networks neglect to capture the inherent attributes of natural images and extract features only in a single scale in the field of image super-resolution reconstruction, a network structure based on attention mechanism and multi-scale feature fusion is proposed. By using the attention mechanism, the network can effectively integrate the non-local information and second-order features of the image, so as to improve the feature expression ability of the network. At the same time, the convolution kernel of different scales is used to extract the multi-scale information of the image, so as to preserve the complete information characteristics at different scales. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms in objective quantitative metrics and visual quality.
Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion
State Key Laboratory of Advanced Rail Autonomous Operation（先进轨道交通自主运行全国重点实验室）；Beijing Jiaotong University（北京交通大学）；School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子与信息工程学院）；Linköping University（林雪平大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction
School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）；Eli Lilly and Company（礼来公司）；Department of Mathematics, Purdue University（普渡大学数学系）
AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data
ShanghaiTech University（上海科技大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
MMR-Mamba: Multi-Modal MRI Reconstruction with Mamba and Spatial-Frequency Information Fusion
The Hong Kong Polytechnic University（香港理工大学）；University of Science and Technology of China（中国科学技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Stanford University（斯坦福大学）
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion
School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；ToolSensing Technologies Co., Ltd（拓深科技有限公司）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院多维信息处理上海重点实验室）
Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions
Institute of Aerospace and System Engineering, National Taipei University of Technology, Taipei, Taiwan（航空航天工程研究所，台北科技大学，台北，台湾）；Department of Information Technology, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam（信息技术系，胡志明市教育大学，胡志明市，越南）
Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning
University of Science and Technology of China（中国科学技术大学）；Stanford University（斯坦福大学）；Centre for Artificial Intelligence and Robotics (CAIR), HKISI-CAS（香港智能科学研究所-人工智能与机器人中心（CAIR））
Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses
City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Caritas Institute of Higher Education（明爱专上学院）；Shanghai Jiao Tong University（上海交通大学）
