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Image and video enhancement through motion based interpolation and nonlocal-means denoising techniques.

机译:通过基于运动的插值和非局部均值去噪技术增强图像和视频。

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摘要

In this research, we investigate advanced image and video enhancement techniques based on motion based interpolation and nonlocal-means (NL-means) denoising. The dissertation consits of three main results. Two video processing applications namely, video error concealment (EC) and frame rate up-conversion (FRUC), based on motion analysis have been examined. Then, an improved NL-means algorithm have been proposed for image denoising. They are detailed below.In the first part of this study, low-complexity error concealment techniques are studied. The boundary matching algorithm (BMA) is an attractive choice for video error concealment due to its low complexity. Here, we examine a variant of BMA called the outer boundary matching algorithm (OBMA). Although BMA and OBMA are similar in their design principle, it is empirically observed that OBMA outperforms BMA by a significant margin (typically, 0.5dB or higher) while maintaining the same level of complexity. We first explain the superior performance of OBMA, and conclude that OBMA provides an excellent tradeoff between the complexity and the quality of concealed video for a wide range of test video sequences and error conditions. In addition, we present two extensions of OBMA, i.e. refined local search and multiple boundary layers. These extensions can be employed to enhance the performance of OBMA at slightly higher computational complexity. Finally, the effect of the exible macroblock ordering (FMO) on the performance of several EC algorithms is examined.In the second part of this work, two challenging situations for video frame rate upconversion (FRUC) are identified and analyzed namely, when the input video clip has abrupt illumination change and a low frame rate. Then, a low-complexity processing technique and robust FRUC algorithm are proposed to address these two issues. The proposed algorithm utilizes a translational motion vector model of the first- and the second-orders and detects the continuity of these motion vectors. Additionally, a spatial smoothness criterion is employed to improve perceptual quality of interpolated frames. The superior performance of the proposed algorithm has been extensively tested and representative examples are given in this work.In the third part of this research, an adaptive image denoising technique based on the NL-means algorithm is proposed. The proposed method employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique to achieve robust block classification in noisy images. Then, a local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm based on the alignment of dominant orientation is adopted for similarity matching. In addition, the noise level can be accurately estimated using block classification and the Laplacian operator. Experimental results are given to demonstrate the superior denoising performance of the proposed adaptive NL-means (ANL-means) denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison, where images corrupted by additive white Gaussian noise (AWGN) and Rician noise are both tested.
机译:在这项研究中,我们研究基于基于运动的插值和非局部均值(NL-means)去噪的高级图像和视频增强技术。本文主要包括三个方面的研究成果。已经研究了基于运动分析的两个视频处理应用程序,即视频错误隐藏(EC)和帧速率上转换(FRUC)。然后,提出了一种改进的NL均值算法进行图像去噪。它们的详细信息如下。在本研究的第一部分,研究了低复杂度的错误隐藏技术。边界匹配算法(BMA)复杂度低,是视频错误隐藏的诱人选择。在这里,我们检查了BMA的一种变体,称为外部边界匹配算法(OBMA)。尽管BMA和OBMA在设计原理上相似,但是从经验上可以观察到,OBMA在保持相同水平的复杂性的同时,以明显的优势(通常为0.5dB或更高)优于BMA。我们首先解释OBMA的卓越性能,并得出结论,对于多种测试视频序列和错误条件,OBMA在隐藏视频的复杂性和质量之间提供了极好的折衷。另外,我们提出了OBMA的两个扩展,即改进的局部搜索和多个边界层。这些扩展可以用来以稍微更高的计算复杂度来增强OBMA的性能。最后,研究了可操作宏块排序(FMO)对几种EC算法性能的影响。在本工作的第二部分中,确定并分析了两种具有挑战性的情况,即视频帧速率上转换(FRUC),即输入时视频剪辑的照度突然变化且帧速率较低。然后,提出了一种低复杂度的处理技术和鲁棒的FRUC算法来解决这两个问题。所提出的算法利用一阶和二阶的平移运动矢量模型并检测这些运动矢量的连续性。另外,采用空间平滑度标准来改善内插帧的感知质量。对该算法的优越性能进行了广泛的测试,并给出了具有代表性的实例。在本研究的第三部分,提出了一种基于NL-means算法的自适应图像去噪技术。所提出的方法采用奇异值分解(SVD)方法和K-均值聚类(K-means)技术在噪声图像中实现鲁棒的块分类。然后,自适应地调整局部窗口以匹配块的局部属性。最后,采用基于主导方向对齐的旋转块匹配算法进行相似度匹配。此外,可以使用块分类和拉普拉斯算子来准确估计噪声水平。实验结果表明,在PSNR和感知质量比较方面,所提出的自适应NL均值(ANL-means)降噪技术优于各种图像降噪基准,其中图像受到加性高斯白噪声(AWGN)的破坏和Rician噪声都经过测试。

著录项

  • 作者

    Thaipanich, Tanaphol.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Engineering, Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 132 p.
  • 总页数 132
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 ;
  • 原文服务方 国家工程技术数字图书馆
  • 关键词

    ;

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