Explore MATLAB source code curated for "图像去噪" with clean implementations, documentation, and examples.
Digital image processing using K-SVD dictionary learning method, sparse and redundant representation theory for signals, featuring MATLAB implementation examples for image compression and image denoising applications with algorithm explanations.
MATLAB-based image denoising program utilizing wavelet transform with practical implementation details
Denoise images by applying sparse coding to local patches using pre-trained overcomplete dictionaries, followed by patch aggregation through averaging. This technique employs sparse and redundant representations over learned dictionaries, as detailed in "Image Denoising Via Sparse and Redundant Representations over Learned Dictionaries." The algorithm involves dictionary initialization, patch extraction, L1-norm optimization for sparse coding (e.g., via Orthogonal Matching Pursuit), and weighted averaging to reconstruct the denoised image.
A comprehensive shearlet transform package with practical examples for image denoising, image fusion, and other advanced image processing applications. The implementation includes optimized algorithms for multi-scale geometric analysis.
Implementation of image denoising through a semi-implicit Additive Operator Splitting (AOS) algorithm applied to the regularized Perona-Malik (P-M) equation in image processing.
This program utilizes MATLAB programming for image denoising through the dual-tree complex wavelet transform (DTCWT) method, providing detailed code implementation for multi-scale analysis and threshold-based noise reduction.
A state-of-the-art algorithm for image denoising with excellent performance and novel implementation approaches
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.
A clear and practical implementation of the BM3D algorithm for image denoising in MATLAB, including PSNR calculation for performance evaluation. This code demonstrates block-matching, 3D transforms, and collaborative filtering techniques to effectively reduce noise while preserving image details.
This study implements image denoising using wavelet and fractal methodologies, followed by a comprehensive comparison of their performance. The implementation includes practical code considerations for both approaches, evaluating noise reduction efficiency and detail preservation capabilities.