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Junrui Lv - IEEE Xplore Author Profile
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Junrui Lv

Also published under: Jun Rui Lv

Publications
4
Citations
5
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (8)
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Junrui Lv

Also published under: Jun Rui Lv

Affiliation

School of Mathematics and Computer Science
Panzhihua University
Panzhihua, Sichuan, China

Publication Topics

Biography

Junrui Lv received the B.S. degree from the School of Computer Science and Engineering, Central South University, China, in 2005, and the M.S. degree from the Software College, University of Electronic Science and Technology, China, in 2009. Her current research interests include logistics optimization and image processing.(Based on document published on 20 January 2025).
Publications
4
Citations
5
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (8)
Author's Published Works

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Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion

Renhan Zhang;Xuegang Luo;Junrui Lv;Junyang Cao;Yangping Zhu;Juan Wang;Bochuan Zheng

IEEE Access
Year: 2025 | Volume: 13 | Journal Article |
This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and similarity among different classes, MSFF enhances the global context perception in the window partitioning framework through Context Modulation Attention (CMA). Meanwhile, it extracts fine-grained local information vi...Show More

Enhancing Medical Image Classification With Context Modulated Attention and Multi-Scale Feature Fusion

Renhan Zhang;Xuegang Luo;Junrui Lv;Junyang Cao;Yangping Zhu;Juan Wang;Bochuan Zheng

Year: 2025 | Volume: 13 | Journal Article |

Multimodal Low-Rank Tensor Subspace Learning for Hyperspectral Image Restoration

Junrui Lv;Xuegang Luo;Juan Wang

IEEE Geoscience and Remote Sensing Letters
Year: 2023 | Volume: 20 | Journal Article |
Cited by: Papers (1)
The restoration of hyperspectral images (HSIs) is a crucial process that eliminates various types of noise to improve subsequent applications. To effectively utilize the inherent low-rank and spatial smoothness of HSI data, this letter proposes a method that employs multimodal low-rank tensor subspace learning with total variation regularization (MLTSL-TV) model to denoise HSI data based on the ob...Show More

Multimodal Low-Rank Tensor Subspace Learning for Hyperspectral Image Restoration

Junrui Lv;Xuegang Luo;Juan Wang

Year: 2023 | Volume: 20 | Journal Article |

Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

Luo Xuegang;Junrui Lv;Juan Wang

IEEE Geoscience and Remote Sensing Letters
Year: 2022 | Volume: 19 | Journal Article |
Cited by: Papers (3)
Hyperspectral imagery (HSI) restoration is a fundamental problem as a preprocessing step. In this letter, we present a novel auto-weighted nonlocal tensor ring rank minimization (ANTRRM) to reduce noise in HSI. First, nonlocal cuboid tensorization (NCT), built by similar grouping cuboids in HSI data, exploits the nonlocal self-similarity and the spatial–spectral correlation simultaneously. Then, t...Show More

Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

Luo Xuegang;Junrui Lv;Juan Wang

Year: 2022 | Volume: 19 | Journal Article |

An Approach for Triangle Traffic Sign Detection Based on Improved Radon Transform

Jun Rui Lv;Xue Gang Luo

2012 Fourth International Conference on Computational and Information Sciences
Year: 2012 | Conference Paper |
Cited by: Papers (1) | Patents (2)
Aimed at the complexity of the traffic, this paper presents a triangle traffic sign detection method using Radon transform . the process of detection consists of three parts. Firstly, the background in image is filtered by the color characteristics of triangle traffic sign. Then the target image is transformed from two-dimensional space to Radon projection space. Finally, triangle traffic signs in...Show More

An Approach for Triangle Traffic Sign Detection Based on Improved Radon Transform

Jun Rui Lv;Xue Gang Luo

Year: 2012 | Conference Paper |

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