Example of Wavelet Denoising: Decomposition and Reconstruction
This example demonstrates the decomposition and reconstruction process for wavelet denoising, including data files and code implementation details.
Estimation of Threshold Values at Various Scales
In partial discharge testing, acquired signals often contain white noise and periodic interference that need removal. This implementation utilizes the commonly used db6 wavelet from the Daubechies series to perform a 9-level multiresolution decomposition. Based on the energy characteristics of white noise, threshold values for each scale are estimated using hard thresholding processing, followed by signal reconstruction. The algorithm involves wavelet decomposition, noise variance estimation, and threshold application using MATLAB's wdenoise function or custom implementation with wthresh.
3D Model Reconstruction of Buildings from 2D Images
Reconstructing 3D building models using photographs captured by cameras, involving Structure from Motion (SfM) algorithms and multi-view stereo techniques.
Morphological Processing Including Opening Operations
Morphological Processing with MATLAB source code: Opening operations, closing operations, edge detection, reconstruction, and more
Ridgelet Multiscale Transform
MATLAB implementation of ridgelet multiscale transform for image analysis, reconstruction, and compression with detailed algorithm explanations
Harmonic Wavelet Decomposition and Reconstruction Implementation
This MATLAB source code implements harmonic wavelet decomposition and reconstruction algorithms for signal analysis.
3D Reconstruction Technology
3D reconstruction technology represents a crucial field in computer vision research, with stereo vision serving as a fundamental technique that utilizes stereo image pairs captured by left and right cameras to reconstruct 3D scene information based on geometric relationships, often implemented through disparity mapping and triangulation algorithms.
Applications of Wavelets in Signal Processing
Wavelet applications in signal processing, including signal decomposition and reconstruction using wavelet transforms, along with noise thresholding techniques for signal enhancement.
Singular Value Decomposition for Images
Singular Value Decomposition of images with reconstruction techniques to demonstrate the transformation effects
Image Denoising Using Dyadic Wavelet Decomposition and Reconstruction
This function implements image denoising through dyadic wavelet decomposition and reconstruction, employing multi-scale analysis to separate noise from image features while preserving important details.