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Frédéric Magoulès - IEEE Xplore Author Profile

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Author details

Frédéric Magoulès

Also published under: Frederic Magoules, Frédéric Magoules, Frédéric Magoulés

Publications

63

Citations

426

Publications by Year

20082022

Co-Authors:

José AlvesEsteban AntierGuillaume Gbikpi BenissanPatrick BourdotFrancesca Bugiotti

Show All Co-Authors (61)

Frédéric Magoulès

Also published under: Frederic Magoules, Frédéric Magoules, Frédéric Magoulés

Affiliation

Université Paris-Saclay

CentraleSupélec

MICS, Gif-sur-Yvette, France

Publication Topics

Parallelization,
Convolutional Neural Network,
Finite Element,
Finite Element Method,
Gradient Method,
Graph Convolutional Network,
Linear System,
Multilayer Perceptron,
Neural Network,
Artificial Neural Network,
Deep Learning Models,
Domain Decomposition

Biography

Frédéric Magoulès received the BSc degree in engineering sciences, the MSc degree in applied mathematics, the MSc degree in numerical analysis, and the PhD degree in applied mathematics from the Université Pierre & Marie Curie, France, in 1993, 1994, 1995, and 2000, respectively. He is currently a professor in the Department of Mathematics and in the Department of Computer Science, CentraleSupélec, Université Paris-Saclay, France. His research interests include parallel computing and data mining. He is a fellow of IMA and BCS, and a member of the ACM, SIAM, and IEEE.(Based on document published on 4 May 2016).

Publications

63

Citations

426

Publications by Year

20082022

Co-Authors:

José Alves
Esteban Antier
Guillaume Gbikpi Benissan
Patrick Bourdot
Francesca Bugiotti

Show All Co-Authors (61)

Author's Published Works

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Showing 1-25 of 25 results

Journals (14)

Conferences (9)

Early Access Articles (2)

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Jiaming Wang(25)
Tao Lu(20)
Zhenfeng Shao(13)
Xiao Huang(10)
Ruiqian Zhang(9)
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China(10)
Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, China(10)
School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China(8)
Department of Geosciences, University of Arkansas, Fayetteville, AR, USA(6)
Wuhan Institute of Technology, Wuhan, China(4)
IEEE Transactions on Geoscience and Remote Sensing(5)
2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)(4)
IEEE Geoscience and Remote Sensing Letters(4)
IEEE Transactions on Multimedia(2)
2021 IEEE International Conference on Image Processing (ICIP)(1)
IEEE(25)
Wuhan, China(5)
Anchorage, AK, USA(1)
Athens, Greece(1)
Seattle, WA, USA(1)
Shenzhen, China(1)
Convolutional Neural Network(15)
Feature Maps(10)
Super-resolution(8)
Loss Function(7)
Convolutional Layers(6)

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Results

TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection

Yuankun Wang;Zhenfeng Shao;Jiaming Wang;Yu Wang;Yulin Ding;Gui Cheng

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

Detecting vulnerable road users (VRUs) at night presents significant challenges. Numerous methods rely heavily on annotations, yet the low visibility of nighttime images poses difficulties for labeling. To obviate the need for nighttime annotations, unsupervised domain adaptation manifests as a viable solution. However, existing approaches primarily focus on semantic-level domain gaps, often overl...Show More

TripleA: An Unsupervised Domain Adaptation Framework for Nighttime VRU Detection

Yuankun Wang;Zhenfeng Shao;Jiaming Wang;Yu Wang;Yulin Ding;Gui Cheng

IEEE Transactions on Intelligent Transportation Systems

Year: 2025 | Early Access Article |

Rethinking the Role of Panchromatic Images in Pan-sharpening

Jiaming Wang;Xitong Chen;Xiao Huang;Ruiqian Zhang;Yu Wang;Tao Lu

IEEE Transactions on Multimedia

Year: 2025 | Early Access Article |

Recent pan-sharpening methods have predominantly utilized techniques tailored for natural image scenes, often overlooking the unique features arising from non-overlapping spectral responses. In light of this, we have reevaluated the utility of panchromatic (PAN) images and introduced a theory anchored in the spectral response of satellite sensors. This posits that a PAN image is effectively a line...Show More

Rethinking the Role of Panchromatic Images in Pan-sharpening

Jiaming Wang;Xitong Chen;Xiao Huang;Ruiqian Zhang;Yu Wang;Tao Lu

IEEE Transactions on Multimedia

Year: 2025 | Early Access Article |

Targetless Extrinsic Calibration for LiDAR and Camera Systems with Intensity Information

Weiyang Jiang;Yanduo Zhang;Tao Lu;Yunxiao Hu;Jiaming Wang

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

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Accurate and efficient external calibration, available in real-time within a targetless environment, remains a significant challenge due to the inherent inhomogeneous reflectance, density distribution, and small FOV of Non-Repeat Scanning (NRS) LiDAR system data, these issues bring difficulties for subsequent applications in areas such as simultaneously localization and mapping (SLAM) and autonomo...Show More

Targetless Extrinsic Calibration for LiDAR and Camera Systems with Intensity Information

Weiyang Jiang;Yanduo Zhang;Tao Lu;Yunxiao Hu;Jiaming Wang

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

Temporal Action Detection with Frequency Attention Mechanism

Wanghui Wu;Tao Lu;Jiaming Wang;Pan Tang;Fangqun Gao

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

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Due to the variability of video length and action duration, the temporal action detection task faces the problem of blurred action boundaries that are difficult to capture accurately. To alleviate this problem, this paper proposes a Frequency Attention Mechanism (FAM) that adaptively models the frequency dependencies between video signal channels, enabling the model to better understand the freque...Show More

Temporal Action Detection with Frequency Attention Mechanism

Wanghui Wu;Tao Lu;Jiaming Wang;Pan Tang;Fangqun Gao

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

Temporal Segment Fourier Transformer for Aciton Quality Assessment

Jifeng Han;Yanduo Zhang;Pan Tang;Tao Lu;Jiaming Wang;Fangqun Gao

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

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Action Quality Assessment (AQA) plays an important role in video analysis, and unlike action recognition tasks, AQA tasks are more challenging because they require attention to subtle differences within the class. In order to capture fine-grained temporal semantic information, we designed a temporal segmentation transformer (TST) using the powerful sequence modeling capability of transformer to pa...Show More

Temporal Segment Fourier Transformer for Aciton Quality Assessment

Jifeng Han;Yanduo Zhang;Pan Tang;Tao Lu;Jiaming Wang;Fangqun Gao

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

A Multi-Robot 3D Point Cloud Map Merging Method Based on Overlapping Region

Ke Xu;Tao Lu;Jiaming Wang;Pan Tang

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

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In multi-robot mapping, differences in sensor capabilities across various robotic platforms can result in significant discrepancies in the generated maps. A more critical issue is the large displacement between maps, making it difficult to determine the transformation relationships between them accurately. Additionally, multi-robot SLAM often requires processing a larger number of points, leading ...Show More

A Multi-Robot 3D Point Cloud Map Merging Method Based on Overlapping Region

Ke Xu;Tao Lu;Jiaming Wang;Pan Tang

2024 7th International Conference on Mechatronics, Robotics and Automation (ICMRA)

Year: 2024 | Conference Paper |

MF-BHNet: A Hybrid Multimodal Fusion Network for Building Height Estimation Using Sentinel-1 and Sentinel-2 Imagery

Siyuan Wang;Bowen Cai;Dongyang Hou;Qing Ding;Jiaming Wang;Zhenfeng Shao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Integrated Sentinel-1 synthetic aperture radar (SAR) imagery and Sentinel-2 optical imagery have shown great promise in mapping large-scale building height. Effectively fusing the complementary features of SAR and optical imagery is a key challenge in enhancing the building height estimation performance. However, SAR imagery and optical imagery have significant heterogeneity, which makes obtaining...Show More

MF-BHNet: A Hybrid Multimodal Fusion Network for Building Height Estimation Using Sentinel-1 and Sentinel-2 Imagery

Siyuan Wang;Bowen Cai;Dongyang Hou;Qing Ding;Jiaming Wang;Zhenfeng Shao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cross-view Aggregation Network For Stereo Image Super-Resolution

Zhitao Chen;Tao Lu;Kanghui Zhao;Bolin Zhu;Zhen Li;Jiaming Wang;Yanduo Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2024 | Conference Paper |

Cited by: Papers (3)

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Although stereo image super-resolution has been extensively studied, many existing works only rely on attention in a single epipolar direction to reconstruct stereo images. In the case of asymmetric parallax images, these methods often struggle to capture reliable stereo correspondence, resulting in reconstructed images suffering from blurring and artifacts. In this paper, we propose a novel metho...Show More

Cross-view Aggregation Network For Stereo Image Super-Resolution

Zhitao Chen;Tao Lu;Kanghui Zhao;Bolin Zhu;Zhen Li;Jiaming Wang;Yanduo Zhang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2024 | Conference Paper |

Change Dino: A Unified Transformer-Based Framework For Object-Level Change Detection and Segmentation in Remote Sensing Imagery

Yixin Chen;Ruiqian Zhang;Xiaogang Ning;Hanchao Zhang;You He;Yuxing Xie;Jiaming Wang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

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In the realm of remote sensing change detection, deep learning-based pixel-level methods have shown commendable accuracy and speed. However, due to the difficulty in distinguishing between each changed object and the high matching accuracy required, there are still limitations in practical applications. To address these issues, we propose Change DINO, a novel unified object-level change detection ...Show More

Change Dino: A Unified Transformer-Based Framework For Object-Level Change Detection and Segmentation in Remote Sensing Imagery

Yixin Chen;Ruiqian Zhang;Xiaogang Ning;Hanchao Zhang;You He;Yuxing Xie;Jiaming Wang

IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Year: 2024 | Conference Paper |

A Deep Error Removal Network for Pan-Sharpening

Jiaming Wang;Tao Lu;Xiao Huang;Ruiqian Zhang;Dongyue Luo

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

Cited by: Papers (1)

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The phenomenon of nonoverlapping spectral responses is an inevitable but an overlooked problem in the deep-learning-based panchromatic (PAN) and multispectral (MS) images’ fusion task, which will introduce some error information from the PAN image. In light of this, we construct a novel prior model based on spectral response theory and develop a model-based pan-sharpening network. Specifically, we...Show More

A Deep Error Removal Network for Pan-Sharpening

Jiaming Wang;Tao Lu;Xiao Huang;Ruiqian Zhang;Dongyue Luo

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

Hyper-Laplacian Prior for Remote Sensing Image Super-Resolution

Kanghui Zhao;Tao Lu;Jiaming Wang;Yanduo Zhang;Junjun Jiang;Zixiang Xiong

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Image explicit prior has made breakthrough progress in the super-resolution (SR) due to the additional supervisory information provided. However, existing explicit prior-guided SR methods directly use the Gaussian gradient or Laplacian gradient prior, which cannot fit the gradient distribution of remote sensing images. Through the statistics of gradient probability density distribution of the remo...Show More

Hyper-Laplacian Prior for Remote Sensing Image Super-Resolution

Kanghui Zhao;Tao Lu;Jiaming Wang;Yanduo Zhang;Junjun Jiang;Zixiang Xiong

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Remote Sensing Pan-Sharpening via Cross-Spectral–Spatial Fusion Network

Yu Wang;Zhenfeng Shao;Tao Lu;Jiaming Wang;Gui Cheng;Xiaolong Zuo;Chaoya Dang

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

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Pan-sharpening is a technique used to create high-resolution multispectral (HRMS) images by merging low-resolution multispectral (LRMS) images with corresponding high-resolution panchromatic (PAN) images. Despite achieving state-of-the-art performance, existing panchromatic sharpening networks based on deep-learning (DL) methods suffer from spectral distortion and insufficient spatial texture enha...Show More

Remote Sensing Pan-Sharpening via Cross-Spectral–Spatial Fusion Network

Yu Wang;Zhenfeng Shao;Tao Lu;Jiaming Wang;Gui Cheng;Xiaolong Zuo;Chaoya Dang

IEEE Geoscience and Remote Sensing Letters

Year: 2024 | Volume: 21 | Journal Article |

EdgeAware YOLO: Enhancing Small Object Edge Detection in Base Station Equipment Inspection

Xianhong Zhang;Tao Lu;Jiaming Wang;Aibo Xu;Jijun Xu

2023 International Conference on Artificial Intelligence Innovation (ICAII)

Year: 2023 | Conference Paper |

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The small object detection task has always presented a significant challenge in the field of object detection research. However, existing methods often struggle to effectively focus on the blurred edges of small objects, leading to overlapping predicted bounding boxes or inaccurate predictions for similar small objects in dense scenarios. In this paper, we propose a novel small object detection ne...Show More

EdgeAware YOLO: Enhancing Small Object Edge Detection in Base Station Equipment Inspection

Xianhong Zhang;Tao Lu;Jiaming Wang;Aibo Xu;Jijun Xu

2023 International Conference on Artificial Intelligence Innovation (ICAII)

Year: 2023 | Conference Paper |

Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism

Xitong Chen;Yuntao Wu;Tao Lu;Quan Kong;Jiaming Wang;Yu Wang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2023 | Volume: 16 | Journal Article |

Cited by: Papers (4)

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Recently, deep-learning-based methods have become the current mainstream of remote sensing image super-resolution (SR) due to their powerful fitting ability. However, they are still unsatisfactory in large-scale factor SR scenarios. The more complicated information distribution of images further increases the difficulty of reconstruction. In this article, we propose a novel residual split attentio...Show More

Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism

Xitong Chen;Yuntao Wu;Tao Lu;Quan Kong;Jiaming Wang;Yu Wang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2023 | Volume: 16 | Journal Article |

Remote Sensing Image Super-Resolution via Multiscale Enhancement Network

Yu Wang;Zhenfeng Shao;Tao Lu;Changzhi Wu;Jiaming Wang

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

Cited by: Papers (22)

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In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we prop...Show More

Remote Sensing Image Super-Resolution via Multiscale Enhancement Network

Yu Wang;Zhenfeng Shao;Tao Lu;Changzhi Wu;Jiaming Wang

IEEE Geoscience and Remote Sensing Letters

Year: 2023 | Volume: 20 | Journal Article |

A Deep Unfolding Method for Satellite Super Resolution

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang

IEEE Transactions on Computational Imaging

Year: 2022 | Volume: 8 | Journal Article |

Cited by: Papers (8)

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Despite that existing deep-learning-based super resolution methods for satellite images have achieved great performance, these methods are generally designed to stack unaccountable and dense modules (i.e., residual blocks and dense blocks) to reach an optimal mapping function between low-resolution and high-resolution patches/images at the expense of computing resources. To address this challenge,...Show More

A Deep Unfolding Method for Satellite Super Resolution

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang

IEEE Transactions on Computational Imaging

Year: 2022 | Volume: 8 | Journal Article |

From Artifact Removal to Super-Resolution

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Yong Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (14)

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Deep-learning-based super-resolution (SR) methods have been extensively studied and have achieved significant performance with deep convolutional neural networks. However, the results still suffer from the ringing effect, especially in satellite image SR tasks, due to the loss of image details in the satellite degradation process. In this article, we build a novel satellite SR framework by decompo...Show More

From Artifact Removal to Super-Resolution

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Yong Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Pan-Sharpening via Deep Locally Linear Embedding Residual Network

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Gui Cheng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (5)

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The goal of pan-sharpening tasks is to fuse panchromatic (PAN) images and low-spatial-resolution (LR) multispectral (MS) images for the purpose of aggregating texture and spectral information. Although traditional embedding-based pan-sharpening methods achieve competitive results, they are limited by the shallow network and not suitable for large-scale datasets. In this study, we design a novel mu...Show More

Pan-Sharpening via Deep Locally Linear Embedding Residual Network

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Gui Cheng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

SSCAN: A Spatial–Spectral Cross Attention Network for Hyperspectral Image Denoising

Zhiqiang Wang;Zhenfeng Shao;Xiao Huang;Jiaming Wang;Tao Lu

IEEE Geoscience and Remote Sensing Letters

Year: 2022 | Volume: 19 | Journal Article |

Cited by: Papers (10)

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Hyperspectral images (HSIs) have been widely used in a variety of applications thanks to the rich spectral information they are able to provide. Among all HSI processing tasks, HSI denoising is a crucial step. Recent years have seen great progress in deep learning-based image denoising methods. However, existing efforts tend to ignore the correlations between adjacent spectral bands, leading to pr...Show More

SSCAN: A Spatial–Spectral Cross Attention Network for Hyperspectral Image Denoising

Zhiqiang Wang;Zhenfeng Shao;Xiao Huang;Jiaming Wang;Tao Lu

IEEE Geoscience and Remote Sensing Letters

Year: 2022 | Volume: 19 | Journal Article |

A Dual-Path Fusion Network for Pan-Sharpening

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (25)

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Most existing deep learning-based pan-sharpening methods own several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement. To address these challenges in pan-sharpening, we propose a novel dual-path fusion network (DPFN). The proposed DPFN includes two major components: 1) the global subnetwork (GSN) and 2) the local subnetwork (LSN). In particular, GS...Show More

A Dual-Path Fusion Network for Pan-Sharpening

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic

Zhenfeng Shao;Gui Cheng;Jiayi Ma;Zhongyuan Wang;Jiaming Wang;Deren Li

IEEE Transactions on Multimedia

Year: 2022 | Volume: 24 | Journal Article |

Cited by: Papers (94)

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Coronavirus Disease 2019 (COVID-19) is a highly infectious virus that has created a health crisis for people all over the world. Social distancing has proved to be an effective non-pharmaceutical measure to slow down the spread of COVID-19. As unmanned aerial vehicle (UAV) is a flexible mobile platform, it is a promising option to use UAV for social distance monitoring. Therefore, we propose a lig...Show More

Real-Time and Accurate UAV Pedestrian Detection for Social Distancing Monitoring in COVID-19 Pandemic

Zhenfeng Shao;Gui Cheng;Jiayi Ma;Zhongyuan Wang;Jiaming Wang;Deren Li

IEEE Transactions on Multimedia

Year: 2022 | Volume: 24 | Journal Article |

Pan-Sharpening Via High-Pass Modification Convolutional Neural Network

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Jiayi Ma

2021 IEEE International Conference on Image Processing (ICIP)

Year: 2021 | Conference Paper |

Cited by: Papers (8)

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Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification b lock. Different from existing methods, the proposed block is designed to learn the high-pass information, leading to enhance spatia...Show More

Pan-Sharpening Via High-Pass Modification Convolutional Neural Network

Jiaming Wang;Zhenfeng Shao;Xiao Huang;Tao Lu;Ruiqian Zhang;Jiayi Ma

2021 IEEE International Conference on Image Processing (ICIP)

Year: 2021 | Conference Paper |

Unsupervised Remoting Sensing Super-Resolution via Migration Image Prior

Jiaming Wang;Zhenfeng Shao;Tao Lu;Xiao Huang;Ruiqian Zhang;Yu Wang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (9)

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Recently, satellites with high temporal resolution have fostered wide attention in various practical applications. Due to limitations of bandwidth and hardware cost, however, the spatial resolution of such satellites is considerably low, largely limiting their potentials in scenarios that require spatially explicit information. To improve image resolution, numerous approaches based on training low...Show More

Unsupervised Remoting Sensing Super-Resolution via Migration Image Prior

Jiaming Wang;Zhenfeng Shao;Tao Lu;Xiao Huang;Ruiqian Zhang;Yu Wang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Single Image Super-Resolution via Multi-Scale Information Polymerization Network

Tao Lu;Yu Wang;Jiaming Wang;Wei Liu;Yanduo Zhang

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Cited by: Papers (19)

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Recently, the performances of deep convolution neural networks (CNNs)-based single-image super-resolution (SISR) have been significantly improved. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper networks and ignore the potential relationship between multi-scale features, leading to the limited representation ability of the reconstructed network. To address this...Show More

Single Image Super-Resolution via Multi-Scale Information Polymerization Network

Tao Lu;Yu Wang;Jiaming Wang;Wei Liu;Yanduo Zhang

IEEE Signal Processing Letters

Year: 2021 | Volume: 28 | Journal Article |

Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening

Gui Cheng;Zhenfeng Shao;Jiaming Wang;Xiao Huang;Chaoya Dang

IEEE/CAA Journal of Automatica Sinica

Year: 2022 | Volume: 9, Issue: 11 | Journal Article |

Cited by: Papers (5)

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Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening

Gui Cheng;Zhenfeng Shao;Jiaming Wang;Xiao Huang;Chaoya Dang

IEEE/CAA Journal of Automatica Sinica

Year: 2022 | Volume: 9, Issue: 11 | Journal Article |

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