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SSIM-0.24
- zhou wang提出的结构相似性图像质量评价算法，是一种全参考的质量评价模式-zhou we analyze the dynamic process of the structural similarity of image quality evaluation algorithm, is a reference to the quality of the whole evaluation model
ssim
- 图像的结构相似度，代码来源于Zhou Wang的网站，用 matlab开发。-Image structure similarity, code from Zhou Wang' s website, matlab development.
ssim_index
- Zhou Wang proposed a new idea of Structural Similarity Index (SSIM )for image quality measurement based on the assumption that the HVS is highly adapted to extract structural information from the viewing field. The Mean Structural Similarity Index (M
11111SSIM
- SSIM Index, Version 1.0 Copyright(c) 2003 Zhou Wang All Rights Reserved. The author was with Howard Hughes Medical Institute, and Laboratory for Computational Vision at Center for Neural Science and Courant Institute of Mathematical Scie
software
- 软件工程的作业题整理，如果您正在用周丽娟王华编的软件工程，这些习题都是连同答案的！-Software engineering problem finishing operations, if you are using ZHOU Li-juan Wang for software engineering, these exercises are, together with the answers!
financial
- 网上下载2006年5月由余洋、王威、周银辉设计，周银辉编码，王跃测试，本人修改，添加了排序功能，listview显示图片功能，统计功能，是家庭理财必备程序-Personal financial management system download in May 2006 Youyu Yang, WANG Wei, Zhou Yinhui design, Zhou Yinhui coding, Yue-test, I modify, add the sort function, listview
DE2_NET
- nios 中的网口调试程序，简单明了，用周立功开发板-nios zhong de wang kou tiao shi cheng xu ,sample,using zhou ligong s kai fa ban
Modern_IQA
- 本书为Zhou Wang著，系统阐述了图像质量评价的相关内容，包括FR\HR\NR评价理论。-This book is writtend by Zhou Wang.Mainly about IQA.Including algoethims of FR\HR\NR.
ssim_2004
- 实现了2004年Zhou Wang提出的图像评价方法MSSIM，可用于去噪，融合等图像处理领域，内含2004年IEEE原文。-Simulation image evaluation algorithm- MSSIM proposed by Zhou Wang, it can be used for denoising, image fusion and so on , including source code and documentation.
metrix_uqi
- This an efficient implementation of the algorithm for calculating the universal image quality index proposed by Zhou Wang and Alan C. Bovik. Please refer to the paper "A Universal Image Quality Index" by Zhou Wang and Alan C. Bovik, published in IEEE
mssim
- the ssim is an measurement to assess to the image quality. it is developed by professor zhou wang.
PSMF
- Zhou Wang&David Zhang的递进开关中值滤波的matlab实现-Progressive Switching Median Filter for the Removal of Impulse Noise from Highly Corrupted Images（Zhou Wang&David Zhang）
SSIMSrc-0.24
- the equivalent of Zhou Wang s SSIM matlab code //with K1 = 0.01, K2 = 0.03 and windows = ones(8) since this enables a fast implementation using summed area tables. //the measure is described in "Image quality assessment: From error measurement to
ssim_index
- HVS的许多特性都与图像质量的评价相关，由于HVS非常复杂,Zhou Wang认为，自然图像信号是高度结构化的，图像的像素间有很强的相关性。构信息提取的图像质量评价构相似性理论是因此提出了一种关于图像质量评价的新思想。-HVS in many of the features are relevant to the evaluation of image quality, the HVS is very complex, Zhou, Wang believes that the natural i
SSIM_MSU_VQMT
- 图像质量评价的最新demo，是俄罗斯国立大学视频图像组研发的，demo中有和王周的SSIM对比。-Image quality uation of the new demo, is a Russian video image group National University research, comparative SSIM and Wang Zhou in demo.
ImageEvaluation--chen
- 几篇经典的图像质量评价方法，包括王周的结构相似度（SSIM）及其改进算法（部分结合局部方差等）.-Several image quality uation of classical method, including the structure similarity of Wang Zhou (SSIM) and its improved algorithm (part of the combination of local variance).
DecisionForest_v3.1
- [1] Quan Wang, Yan Ou, A. Agung Julius, Kim L. Boyer and Min Jun Kim, Tracking Tetrahymena Pyriformis Cells using Decision Trees , 2012 21st International Conference on Pattern Recognition (ICPR), Pages 1843-1847, 11-15 Nov. 2012. [2] Q
PAPER5
- 参与式感知系统中基于社会关系的移动节点位置预测算法 于瑞云,夏兴有,李婕，周岩，王兴伟-Participatory sensing system prediction algorithms based on the location of the mobile node of social relations- in zuiun summer Hing have, Li Jie, Zhou Yan, Wang Xingwei
Microsoft-C#编码规范
- 本文档的每一章节都应该感谢Dan Ruder - 微软Principal Escalation Engineer 。 Dan 对本文档进行了斟字酌句的查阅，并根据其20余年的编程经验提供了大量的珍贵评论 。我很荣幸能与他共事。 我同样感谢微软的四位经理，感谢他们对该项目的一贯支持。他们是Vivian Luo，Allen Ding，Felix Wu 和 Mei Liang。 同时，如果没有如下一站式代码示例库项目的关键成员的辛勤付出，本文档必定不会具有现在的技术深度以及完整度，我在这里要感