3D Data Denoising Processing
3D data denoising processing, including 1D and 2D data denoising applications with code implementation approaches
Denoising 2D Images Using Perona-Malik Anisotropic Diffusion Model
Implementation of Perona-Malik anisotropic diffusion model for 2D image denoising, which effectively suppresses noise while preserving important image edges using gradient-based diffusion control.
Wavelet Soft-Threshold Denoising Processing Method
Wavelet soft-threshold denoising processing method with program demonstration and implementation details
Image Segmentation Using Region Growing Method with Denoising and Enhancement
Implementation of region growing algorithm for image segmentation complemented by noise reduction techniques and image enhancement methods to improve overall visual quality
Curvelet Transform Bayesian Estimation Method for Image Denoising
Implementation of a curvelet transform-based Bayesian estimation approach for accurate noise parameter estimation in noisy images, followed by effective denoising processing using multi-scale and directional decomposition techniques.
ECG Signal Denoising Using Wavelet Transform in MATLAB
Implementing wavelet-based denoising for ECG signals in MATLAB, with signal data provided in a TXT file format, featuring code implementation details and wavelet decomposition techniques
Median Filter: Implementing 5x5 Window Denoising for Noise-Corrupted Images
Median Filter: Applying a 5x5 processing window to remove noise from corrupted images through pixel value replacement
Common Shot Gather Seismic Record Synthesis
Implementation of common shot gather seismic record synthesis with noise injection at various ratios, followed by wavelet transform denoising processing, achieving optimal signal-to-noise enhancement results
GPS Single Epoch Signal Deformation Monitoring Data Denoising Processing
GPS single epoch signal deformation monitoring data denoising processing. Includes wavelet soft/hard threshold denoising and median filtering techniques with code implementation insights.
Recording and Analyzing Personal Voice Signals with Code Implementation
Record personal voice signals using audio recording functions. Perform sampling operations with specified rates using functions like `audiorecorder()` in MATLAB. Plot time-domain waveforms using `plot()` and spectrograms using `spectrogram()` functions. Implement noise addition through random signal generation and denoising using filtering techniques like Wiener filtering or wavelet denoising. Compare pre/post-filtering signals through waveform comparison and spectral analysis. Achieve audio playback using `sound()` function with variable sampling rates for fast/slow playback effects.