Explore MATLAB source code curated for "小波系数" with clean implementations, documentation, and examples.
Implementation of 1D signal multiwavelet decomposition program that loads wavelet coefficients from a coefficient file (coef). Requires preprocessing with pre-filtering operations before decomposition.
Conventional wavelet threshold denoising methods operate under the assumption that wavelet coefficients are independent, neglecting their correlations across adjacent scales, which results in an inherent trade-off between noise removal and preservation of useful high-frequency information.
This program implements multi-level wavelet decomposition of images, extracting wavelet coefficients from each sub-band after decomposition using algorithms like Discrete Wavelet Transform (DWT) with functions such as wavedec2() for 2D signals.
This method computes entropy values from wavelet coefficients after wavelet packet decomposition, providing valuable assistance for beginners in signal processing with practical code implementation examples
Implementation of s-transform and t+s transform on wavelet coefficients, along with normalization techniques for transformed coefficients
2D wavelet decomposition employing Donoho's soft thresholding algorithm, which calculates layer-specific threshold values to filter wavelet coefficients through multiscale analysis.
This program implements wavelet zerotree compression coding for JPEG2000, utilizing the principles of wavelet zerotree encoding by leveraging relationships between wavelet coefficients. The implementation handles multi-level wavelet decomposition, zerotree symbol generation, and bit-plane encoding for efficient data compression.
By introducing the noise reduction concept of wavelet adjacent coefficient correlation into redundant second generation wavelets, this paper proposes a denoising method that overcomes the limitation of traditional threshold denoising methods which fail to consider correlations between wavelet coefficients. The implementation involves correlation analysis within wavelet coefficient neighborhoods and adaptive thresholding based on local statistical properties.
Implementation of HAAR wavelet transforms for wavelet coefficient relationship extraction, followed by zerotree encoding for compression and subsequent decompression to reconstruct the original image. Contains minor optimizations that serve as valuable references for similar implementations.
Performs a three-level wavelet decomposition to obtain detail coefficients (d) and approximation coefficients (a), then calculates energy distribution across frequency bands.