Explore MATLAB source code curated for "去噪" with clean implementations, documentation, and examples.
This collection provides MATLAB source code for fundamental image processing operations including color inversion, common filtering techniques, noise reduction methods, and standard transformations with implementation insights.
Speech Enhancement, Cepstral Analysis, Endpoint Detection, Formant Detection, and Noise Reduction Algorithms with MATLAB Implementation Approaches
A comprehensive software package for Total Variation-based image reconstruction, featuring implementations for denoising, inpainting, and deblurring operations. The package employs first-order optimization algorithms and incorporates regularization techniques to preserve edges while removing noise and artifacts. Reference: J. Dahl, P. C. Hansen, S. H. Jensen, and T. L. Jensen, "Algorithms and Software for Total Variation Image Reconstruction via First-Order Methods," Numerical Algorithms, vol. 53, 2010, pp. 67-92.
Comprehensive guide to wavelet transform denoising techniques, including implementation of soft thresholding, hard thresholding, and custom-designed threshold functions. This complete graduation project provides in-depth analysis, code examples, and practical applications for signal processing. The material covers all aspects of wavelet denoising with detailed algorithmic explanations and MATLAB/Python implementation considerations.
The Curvelet Toolbox enables ridgelet-based transformations for edge extraction, noise reduction, and enhancement of digital images through optimized multiscale directional analysis algorithms.
Morphological Component Analysis (MCA) experimental code for image segmentation and denoising, featuring implementations of morphological operations and component separation algorithms
MATLAB implementation of wavelet packet transform for signal analysis and denoising, featuring complete wavelet decomposition and reconstruction procedures with detailed algorithmic explanations
Leveraging the multiscale analysis properties of wavelet transform, this approach improves basic frequency-domain spectral subtraction by applying different thresholds at various wavelet domain scales. It separates voiced and unvoiced sounds during denoising based on their distinct characteristics, preserving unvoiced components to produce fuller-sounding speech while employing adaptive algorithms to further enhance signal quality.
Calculating the Signal-to-Noise Ratio After Denoising - Shared for Collective Discussion and Technical Implementation Approaches
Dual-tree complex wavelet processing program capable of performing 1D, 2D, and 3D denoising operations with enhanced directional selectivity and shift-invariant properties.