计算摄影的有效回归：从色彩管理到全方位超分辨率 ][Efficient Regression for Computational Photography: from Color Management to Omnidirectional Superresolution]

计算摄影的有效回归：从色彩管理到全方位超分辨率 Efficient Regression for Computational Photography: from Color Management to Omnidirectional Superresolution


课程网址:	http://videolectures.net/nipsworkshops2011_gupta_omnidirectional/
主讲教师:	Maya Gupta
开课单位:	华盛顿大学
开课时间:	2012-01-23
课程语种:	英语
中文简介:	许多计算摄影应用可以被构造为低维回归问题，其需要快速评估测试样本以进行渲染。在这种情况下，将样本存储在可以快速插值的网格或网格上通常是一种实用的方法。我们展示了如何在给定非晶格数据点的情况下最优地求解这种晶格。得到的晶格回归快速而准确。我们展示了它对两种应用的有用性：色彩管理和全方位图像的超分辨率。
课程简介:	Many computational photography applications can be framed as low-dimensional regression problems that require fast evaluation of test samples for rendering. In such cases, storing samples on a grid or lattice that can be quickly interpolated is often a practical approach. We show how to optimally solve for such a lattice given non-lattice data points. The resulting lattice regression is fast and accurate. We demonstrate its usefulness for two applications: color management, and superresolution of omnidirectional images.
关键词:	低维回归; 非晶格数据点; 样本
课程来源:	视频讲座网
最后编审:	2019-09-07：lxf
阅读次数:	65

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