Scholarlink

This chapter discusses the discrete wavelet transform (DWT) and its implementation using filter banks. At first, quadrature mirror filter (QMF) bank, which ensures perfect reconstruction while splitting signals into low-pass and high-pass components, is discussed in detail. The design of M channel filter banks is then introduced, extending the two channel QMF to multiple bands for finer frequency resolution. The chapter further explores tree structured filter banks, where repeated decomposition enables a hierarchical multiresolution representation. Finally, the realization of DWT using filter banks is examined, detailing convolution, downsampling, and recursive filtering techniques for efficient computation. These concepts provide the foundation for practical wavelet-based signal processing.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Discrete Wavelet Transform and Filter Bank Theory

  • 章节

  • 发表日期：

    2025年

查看全文
  • M S Sinith,
  • Gayathri A,
  • Chithra K R

摘要

This chapter discusses the discrete wavelet transform (DWT) and its implementation using filter banks. At first, quadrature mirror filter (QMF) bank, which ensures perfect reconstruction while splitting signals into low-pass and high-pass components, is discussed in detail. The design of M channel filter banks is then introduced, extending the two channel QMF to multiple bands for finer frequency resolution. The chapter further explores tree structured filter banks, where repeated decomposition enables a hierarchical multiresolution representation. Finally, the realization of DWT using filter banks is examined, detailing convolution, downsampling, and recursive filtering techniques for efficient computation. These concepts provide the foundation for practical wavelet-based signal processing.