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Wavelet domain partition-based signal processing with applications to image denoising and compression.

机译：基于小波域分区的信号处理及其在图像去噪和压缩中的应用。

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摘要

This dissertation addresses the problems of image denoising and compression. Image denoising and compression are the fundamental problems in image processing, and often use transform techniques. In this dissertation, the wavelet transform is used and new signal processing techniques are applied in the wavelet domain. The sparsity of the wavelet transform, i.e., a signal energy is concentrated on few large magnitude coefficients, is a main property exploited by wavelet based image processing applications. The new methods for image denoising problem introduced in this dissertation exploits the directional structure of the two-dimensional wavelet transform. For image compression, correlations between adjacent scales (levels) and coefficient pixels, i.e., inter and intra-correlation, are adaptively exploited in the proposed algorithm.; The most frequently used technique in image denoising problems is a thresholding operation. Thresholding the wavelet coefficients exploits the sparse property of the wavelet transform. Applying thresholding to all coefficients uniformly, however, produces oversmoothing of edges and undersmoothing in uniform regions. Recently, adaptive wavelet thresholding utilizing spatial and adjacent scale correlations has been shown to yield good results. This dissertation introduces a related, but more direct, technique for adaptively processing wavelet coefficients based on partitioning of the coefficient space. In the wavelet domain, the coefficient space is partitioned through vector quantization and mask functions are used to obtain the denoised wavelet coefficients. This approach is better able to exploit structure in the coefficient domain and presented simulations show that the proposed technique yields superior performance compared with current wavelet denoising methods.; A new embedded wavelet image compression method, using quad-partition-based wavelet domain image compression, is also proposed. The introduced method uses a quad-partition-based embedded image compression scheme on the highest bit plane and structure and combined encoding scheme to exploit the inter and intra correlation in the lower bit planes. The proposed algorithm has lower complexity than the recently reported PCAS algorithm yet produces better performance. The algorithm exploits the sparsity and clustering properties in the wavelet domains for exploiting the intra-correlation and multiresolutional tree structure for exploiting the inter-correlation. On the highest bit planes in the wavelet transform domains, iteratively partitioning the coefficients in the intra-wavelet domains and representing the significance of the partitions with one bit significantly reduces the complexity of arithmetic coding by reducing the number of wavelet coefficient pixels to encode. Further bit plane encoding uses intra-correlation and inter-correlation of the wavelet coefficients. Experimental compressions of the test images show superior compression performance compared with state-of-the-art compression algorithms.; In conclusion, a generalized thresholding method using a partition based mask operation for image denoising and new embedded bit plane coding algorithm for image compression are proposed. The both techniques can be also applied to structured signals such as ECG and medical related signals for denoising and compression.
机译：本文解决了图像去噪和压缩问题。图像去噪和压缩是图像处理中的基本问题，并且经常使用变换技术。本文采用小波变换，在小波域应用了新的信号处理技术。小波变换的稀疏性，即信号能量集中在几个大的幅度系数上，是基于小波的图像处理应用所利用的主要特性。本文提出的图像去噪新方法利用了二维小波变换的方向结构。对于图像压缩，在所提出的算法中自适应地利用相邻尺度（水平）和系数像素之间的相关性，即，内部和内部相关性。在图像去噪问题中最常用的技术是阈值运算。对小波系数进行阈值处理可利用小波变换的稀疏特性。但是，对所有系数均匀地应用阈值处理会导致边缘的过度平滑和均匀区域中的平滑不足。近来，已经证明利用空间和相邻尺度相关性的自适应小波阈值产生良好的结果。本文介绍了一种基于系数空间划分的自适应，小波系数自适应处理技术。在小波域中，通过向量量化对系数空间进行划分，并使用掩码函数来获得去噪的小波系数。这种方法能够更好地利用系数域中的结构，并且仿真结果表明，与当前的小波去噪方法相比，该技术具有更好的性能。提出了一种基于四分区的小波域图像压缩新的嵌入式小波图像压缩方法。引入的方法在最高位平面和结构上使用基于四分区的嵌入式图像压缩方案，并结合编码方案来利用较低位平面的内部和内部相关性。与最近报道的PCAS算法相比，所提出的算法具有更低的复杂度，但产生了更好的性能。该算法利用小波域中的稀疏性和聚类属性来利用内部相关性，并利用多分辨率树结构来利用相互相关性。在小波变换域的最高位平面上，迭代地对小波内域中的系数进行分区，并用一位表示分区的重要性，这通过减少要编码的小波系数像素的数量而大大降低了算术编码的复杂性。进一步的位平面编码使用小波系数的内相关和内相关。与最新的压缩算法相比，测试图像的实验压缩显示出卓越的压缩性能。综上所述，提出了一种基于分区的掩码操作进行图像去噪的广义阈值方法和新的用于图像压缩的嵌入式位平面编码算法。两种技术还可以应用于诸如ECG的结构化信号和用于降噪和压缩的医学相关信号。

著录项

  • 作者

    Kim, Il-Ryeol.;

  • 作者单位

    University of Delaware.;

  • 授予单位 University of Delaware.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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