报告题目：A Novel Neural CSTV Model Joint with Quaternion-Based Group Sparse Representation for Color Image Inpainting from Structured Corruptions

报告人：江苏师范大学 贾志刚教授

报告时间：2026年8月10日 10:00-11:00

报告地点：星空体育106会议室

报告摘要：Most of color image restoration models overlook the valid information across color spaces and the coupling between color channels, which are essential properties. To address such limitations, we present a new two-stage image restoration model: Neural cross-space total variation regularization model with quaternion-based group sparse representation (QGS-NCSTV). In the first stage, a novel neural cross-space total variation regularization functional effectively captures the cross-space information to restore color images by incorporating the perceptual characteristics of the HSV space and the structural correlations of the RGB space. In the second stage, the quaternion-based group sparse representation preserves the coupling between color channels with leveraging nonlocal self-similarity. Experimental results show QGS-NCSTV outperforms the state-of-the-art methods in terms of PSNR, SSIM and UQI, where the reflection in colonoscopy images is successfully solved.

报告人简介：贾志刚，江苏师范大学数学与统计星空体育、数学研究院，教授、博导。2009年毕业于华东师范大学数学系，获理学博士学位；2023年入选江苏高校“青蓝工程”中青年学术带头人；2024年起担任学术期刊Numerical Algorithms的编委。主要研究方向为数值代数与图像处理，至今已在IEEE Trans. Image Process.，SIAM J. Matrix Anal. Appl., SIAM J. Sci. Comput., SIAM J. Imaging Sci. 等期刊上发表学术论文50余篇，其中 4 篇入选“ESI高被引”论文；在科学出版社（北京）出版英文专著1部（独立作者）；主持国家自然科学基金项目3项、省高校自然科学研究重大项目1项，参加国家自然科学基金重大项目和国家重点研发计划课题各1项。曾到英国曼彻斯特大学、香港浸会大学、澳门大学等高校数学系进行学术访问。