多尺度分解 Resources

Showing items tagged with "多尺度分解"

Based on the energy distribution characteristics of wavelet transform and noisy signals, we propose a method that first decomposes noisy images using multi-scale wavelet transform, calculates the noise variance and thresholds for high-frequency coefficients at each scale, processes the high-frequency coefficients using these scale-specific thresholds, and then reconstructs the image using wavelet coefficients to achieve effective image denoising.

MATLAB 281 views Tagged
Download

Wavelet analysis represents a sophisticated branch of signal processing where wavelet transforms enable critical applications including image compression, vibration signal decomposition and reconstruction. Compared to Fourier transforms, wavelet transformations operate as local transforms in both spatial and frequency domains, allowing efficient information extraction from signals. Through fundamental operations like scaling and translation, wavelet transforms achieve multi-scale signal decomposition and reconstruction, effectively overcoming many limitations of Fourier analysis. As a new mathematical discipline, wavelet analysis synthesizes functional analysis, Fourier analysis, and numerical analysis, serving as a powerful "time-scale" analysis and multi-resolution analysis technique with extensive applications across signal processing, speech synthesis, image compression, and pattern recognition.

MATLAB 261 views Tagged
Download