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A Patch-Number and Bandwidth Adaptive Non-Local Kernel Regression Algorithm For Multiview Image Denoising

机译:一种用于多视图图像去噪的补丁数和带宽自适应非局部核回归算法

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摘要

This paper presents an automatic patch number selection method for bandwidth adaptive non-local kernel regression (BA-NLKR) algorithm, which was recently proposed for improving the performance of conventional non-local kernel regression (NLKR) in image processing. Although BA-NLKR addressed the important issue of bandwidth selection, the number of non-local patches, which impacts the integration of local and non-local information, however is chosen empirically. In this paper, we propose a new algorithm for automatic patch number selection based on the intersecting confidence intervals (ICI) rule in order to achieve better performance. Moreover, the proposed patch number and bandwidth adaptive NLKR (PBA-NLKR) is applied to the denoising problem of multiview images. The effectiveness of the proposed algorithm is illustrated by experimental results on denoising for both single-view and multi-view images.
机译:本文提出了一种用于带宽自适应非局部核回归(BA-NLKR)算法的自动补丁数选择方法,该算法最近被提出来提高图像处理中传统非局部核回归(NLKR)的性能。尽管BA-NLKR解决了带宽选择的重要问题,但是非本地补丁的数量会影响本地和非本地信息的集成,但是,它是凭经验选择的。在本文中,我们提出了一种基于相交置信区间(ICI)规则的自动补丁编号选择新算法,以实现更好的性能。此外,将提出的补丁数量和带宽自适应NLKR(PBA-NLKR)应用于多视图图像的去噪问题。通过针对单视图和多视图图像进行去噪的实验结果说明了该算法的有效性。

著录项

  • 作者

    Wu JF; Lin ZC; Wang C; Chan SC;

  • 作者单位
  • 年度 2014
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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