Wavelet Threshold Denoising
Wavelet Threshold Denoising: Implementation techniques and applications in signal processing
Wavelet Denoising
During wavelet decomposition across different scales, signals and noise exhibit fundamentally different propagation characteristics: the modulus maxima of noise diminishes as wavelet scales increase, while the modulus maxima of signals amplifies with larger scales. This principle enables noise removal from signals by reconstructing the original signal using denoised modulus maxima, achieving effective noise suppression. In implementation, this typically involves multiscale decomposition using wavelet transforms, thresholding operations on coefficients, and signal reconstruction.
Image Restoration Using Markov Random Fields
Image restoration based on Markov Random Fields, including program implementation for generating MRF models and application examples for image denoising
Image Compression, Denoising, Enhancement and Sharpening
This comprehensive image processing program demonstrates techniques for compression, denoising, enhancement and sharpening. Implementation includes: displaying digital image matrix data and its Fourier transform, image compression using 2D discrete cosine transform (DCT), contrast enhancement via grayscale transformation, salt-and-pepper noise removal using medfilt2 2D median filtering, mean filtering with filter2 for noise reduction, adaptive Wiener filtering, five distinct gradient enhancement methods for sharpening, high-pass filtering with mask processing, Butterworth low-pass filter for image smoothing, and Butterworth high-pass filter for sharpening operations.
SAR Image Filtering Methods for Denoising Implementation
SAR Image Filtering Methods for Implementing Denoising in SAR Imagery
Bandelet Denoising with BLS-GSM and Translation-Invariant Methods
Implementation of bandelet-based denoising incorporating BLS-GSM and translation-invariant approaches, with note on significant memory requirements for large image processing
SAR Image Denoising Using Wavelet Transform
Implementation of SAR image denoising through wavelet transform, including experimental report, test images, and MATLAB code implementation with detailed algorithm explanations
Speckle Pattern Denoising Using Wavelet Default Threshold Method
Wavelet default threshold denoising for speckle patterns with phase retrieval implementations using phase-shifting and least-squares methods, including phase unwrapping techniques.
Conventional Wavelet Threshold Denoising Methods
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.
Markov Random Field Denoising Examples with Kernel PCA
Image Denoising Using Markov Random Fields and Kernel Principal Component Analysis