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  1. Optimum Signal Processing

  2. The purpose of this book is to provide an introduction to signal processing methods that are based on optimum Wiener filtering and least-squares estimation concepts.
  3. 所属分类:专业指导

    • 发布日期:2009-12-26
    • 文件大小:4194304
    • 提供者:yuyibintony
  1. NONLINEAR IMAGE PROCESSING AND FILTERING A UNIFIED APPROACH BASED ON VERTICALLYWEIGHTED REGRESSION

  2. A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving pro- perties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates uti
  3. 所属分类:专业指导

    • 发布日期:2010-01-24
    • 文件大小:871424
    • 提供者:zhchtzct
  1. BM3D 目前最流行的去噪算法

  2. Image denoising by sparse 3D transform-domain collaborative ltering BM3D去噪算法的实现,包括程序代码,图片,文档介绍
  3. 所属分类:其它

    • 发布日期:2010-03-03
    • 文件大小:2097152
    • 提供者:avivamy
  1. BM3D去噪算法(附带原论文)下载

  2. Image denoising by sparse 3D transform-domain collaborative ltering.去噪效果好,鲁棒性好。
  3. 所属分类:深度学习

    • 发布日期:2017-08-24
    • 文件大小:5242880
    • 提供者:zhangquan2015
  1. TDOA positioning in NLOS scenarios by particle filtering

  2. TDOA positioning in NLOS scenarios by particle filtering
  3. 所属分类:IT管理

    • 发布日期:2018-04-15
    • 文件大小:1017856
    • 提供者:mydaima
  1. Internet of Things (IoT)-CRC(2018).pdf

  2. Internet of Things (IoT) is the third wave of Internet and is supposed to have a potential to connect about 28 billion items by 2020, ranging from bracelets to cars. The term “IoT,” which was rst proposed by Kevin Ashton, a British technologist, in
  3. 所属分类:网络基础

    • 发布日期:2018-01-14
    • 文件大小:14680064
    • 提供者:windstand
  1. BM3D code:Image denoising by sparse 3D transform-domain collaborative ltering

  2. BM3D code,Image denoising by sparse 3D transform-domain collaborative ltering
  3. 所属分类:其它

    • 发布日期:2019-01-21
    • 文件大小:2097152
    • 提供者:junxuezheng
  1. Label Efficient Semi-Supervised Learning via Graph Filtering.pdf

  2. Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning,astheycanexploittheconnectivitypatternsbetweenlabeled and unlabeled data samples to improve learning performance. However, existing graph
  3. 所属分类:深度学习

    • 发布日期:2019-08-09
    • 文件大小:658432
    • 提供者:qq_31367595