Numerical Methods Using MATLAB 4th Edition(Ch)
标签：
上传时间： 2014-04-07
上传用户：wangdx
Information theory, inference and learning algorithms
标签： 编码
上传时间： 2016-04-12
上传用户：baiyouren
USBD.sys驱动程序 USBD.sys驱动程序 USBD.sys驱动程序
标签： USBD.sys
上传时间： 2016-04-14
上传用户：zycmic
Neural Networks and Deep Learning（简体中文），比较经典的深度学习入门教程。
标签： Networks Learning Neural Deep and 简体中文
上传时间： 2016-11-09
上传用户：zhousui
Q-learning在机器人路径规划中的应用
标签： Q-learning
上传时间： 2018-03-28
上传用户：wangshengmin
强化学习中的Q-Learning在路径规划中的应用
标签： Q-Learning planning path
上传时间： 2018-03-28
上传用户：wangshengmin
AUTO CAD 的自学材料，快捷键使用，常用系统变量及功能
上传时间： 2018-08-13
上传用户：ruzmun
Owen, 塑性计算方法，Computational methods for plasticity
标签： Computational plasticity methods for 计算方法
上传时间： 2019-01-06
上传用户：jununlin
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
标签： Super-resolution Multi-scale Dictionary Single Image for
上传时间： 2019-03-28
上传用户：fullout
The main aim of this book is to present a unified, systematic description of basic and advanced problems, methods and algorithms of the modern con- trol theory considered as a foundation for the design of computer control and management systems. The scope of the book differs considerably from the topics of classical traditional control theory mainly oriented to the needs of automatic control of technical devices and technological proc- esses. Taking into account a variety of new applications, the book presents a compact and uniform description containing traditional analysis and op- timization problems for control systems as well as control problems with non-probabilistic models of uncertainty, problems of learning, intelligent, knowledge-based and operation systems – important for applications in the control of manufacturing processes, in the project management and in the control of computer systems.
上传时间： 2020-06-10
上传用户：shancjb
