100元2小时不限次数电话号码,全国空降200元快餐联系方式,24小时微信快餐妹,全国同城约资源匹配系统

科学研究
学术报告
当前位置: 学院主页 > 科学研究 > 学术报告 > 正文

Continuous Modeling Perspective for Imaging Science

发布时间:2025-06-16 作者: 浏览次数:
Speaker: 赵熙乐 DateTime: 2025年6月23日(周一)上午10:00--11:00
Brief Introduction to Speaker:

赵熙乐,电子科技大学教授、博导,入选四川省学术和技术带头人、四川省天府青城计划。第一/通讯在权威期刊SIAM 系列(SISCSIIMS)IEEE系列(TPAMITIPTSPTNNLS)及顶会CVPR等发表研究工作。研究成果获四川省自然科学一等奖、四川省科技进步一等奖、计算数学会青年优秀论文竞赛二等奖。主持国自然面上项目、四川省杰出青年科学基金项目、华为项目。

Place: 国交2号楼201
Abstract:To tackle inverse problems in imaging science, the regularizer, serving as an indispensable cornerstone in modeling, are usually introduced. In this talk, we will begin by reviewing the classical regularizers, including local regularizers, nonlocal regularizers, and global regularizers. We then will discuss the limitations of classical hand-crafted regularizers (e.g., expressive capability, applicability, and flexibility). To address the above limitations of classical regularizers, we suggest a unified?Continuous Modeling Perspective for imaging science,? which continuously represents discrete data by elegantly leveraging tiny neural networks. This?paradigm allows us to readily deconstruct and reconstruct the classical regularizers, thus ?unleashing the potential of regularizers. Extensive experiments demonstrate the promising performance of the continuous modeling perspective.
主站蜘蛛池模板: 太谷县| 鹿泉市| 东乡族自治县| 当雄县| 桂东县| 乌兰浩特市| 修水县| 长治市| 兰坪| 高平市| 嘉定区| 肇东市| 威信县| 潜江市| 宜昌市| 海丰县| 马公市| 任丘市| 剑阁县| 阜阳市| 乐业县| 刚察县| 牡丹江市| 湄潭县| 沙坪坝区| 维西| 新野县| 合江县| 利辛县| 泊头市| 全州县| 河间市| 井研县| 天水市| 三原县| 府谷县| 固原市| 前郭尔| 富蕴县| 昌都县| 饶河县|