报告人：汪祥 教授
报告题目：A Bayesian Deep Prior-Based Quaternion Matrix Completion for Color Image Inpainting
报告时间：2025年12月6日（周六）下午3：00
报告地点：云龙校区6号楼304会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
汪祥，博士、教授、博士生导师，现任南昌大学数学与计算机学院副院长，南昌大学数学一级学科博士学位点和博士后科研流动站负责人。获批多个省级人才称号，担任中国计算数学分会理事，中国高等教育学会教育数学专委会常务理事, 国家天元数学东南中心江西基地执行主任，国际知名期刊《Computational and Applied Mathematics》的副主编。主要从事数值代数、人工智能与数据科学等领域的研究，在大规模稀疏特征值问题、线性和非线性矩阵方程的数值求解、谱聚类等方面取得了一些成果。目前主持(含完成)国家自然科学基金4项及省部级项目十几项。近几年以第一作者或通讯作者在ACM、JSC、CCP、NLAA等权威期刊上共发表SCI收录论文80多篇。以第一完成人身份获江西省自然科学奖1项和江西省教学成果奖3项。
报告摘要：
Color image inpainting plays an important role in computer vision, which aims to reconstruct missing regions from the available information. Existing quaternion-based deep inpainting methods often struggle to restore both global structure and natural textures, especially when only a single corrupted image is available for training. To address these challenges, we propose BQAE-TV, a novel model that integrates a quaternion fully connected network to capture global features while incorporating total variation regularization to optimize quaternion matrix completion, producing structurally coherent and visually natural images. Furthermore, a Bayesian inference mechanism is employed to regularize the deep image prior and mitigate overfitting. Experiments demonstrate that BQAE-TV outperforms both traditional and state-of the-art methods in terms of visual quality and quantitative metrics, validating its effectiveness and robustness.