Explore MATLAB source code curated for "奇异值分解" with clean implementations, documentation, and examples.
Implementation of SVD-based image compression with detailed experimental documentation including programming methodology, result analysis, and sample output images with code implementation insights.
Denoising with Principal Components After Singular Value Decomposition with Algorithm Implementation Details
Implementing image compression via singular Value Decomposition helps beginners understand practical applications of SVD with code implementation insights and algorithm explanations
This method compresses images through matrix singular value decomposition by first dividing the image into blocks, computing inner products of corresponding pixels within each block to obtain correlation matrix A, performing SVD on A, selecting n (n<N) dominant eigenvectors corresponding to larger eigenvalues, and using inner products between these vectors and image blocks for compression and reconstruction.
Based on the fundamental principles and characteristics of Singular Value Decomposition (SVD), this article presents a method for image compression using SVD. The method is illustrated through simple examples demonstrating the basic compression process and workflow. Practical image processing implemented through MATLAB programming validates the effectiveness of the approach, with detailed explanations of key functions like svd() for matrix decomposition and imshow() for image visualization.
MATLAB program for modern spectral estimation using Singular Value Decomposition Total Least Squares method with comprehensive algorithmic implementation details.
Self-developed MATLAB code implementing MVDR-based signal frequency estimation with singular value decomposition, featuring comprehensive signal preprocessing and frequency component analysis
A new digital image watermarking algorithm is proposed that integrates Discrete Wavelet Transform (DWT) with Singular Value Decomposition (SVD). The algorithm performs wavelet decomposition on the original image, partitions the low-frequency subband obtained from decomposition, and applies SVD to each block. The largest singular value from each block is selected to embed Arnold-scrambled watermark information through quantization.
Filtering data from a MAT file followed by denoising using Singular Value Decomposition. The SVD denoising methodology references literature provided in the attachment, with enhanced descriptions of code implementation approaches and key algorithmic steps.
MATLAB program for SVD that enables noise removal operations on one-dimensional signals, featuring matrix decomposition and signal processing capabilities