Explore MATLAB source code curated for "SVD" with clean implementations, documentation, and examples.
Implementation of linear equation solutions for moving target detection, addressing background suppression and noise reduction in visible light image weak target detection using Singular Value Decomposition (SVD) algorithm with MATLAB code demonstrations
Implementation of Principal Component Analysis (PCA) for Loaded Data Using Singular Value Decomposition (SVD) - Complete Source Code
MATLAB implementation of Singular Value Decomposition (SVD) based image watermarking algorithm with complete code demonstration
SVD program implementation for image singular value decomposition, featuring code structure, key functions, and practical applications in image analysis
A watermarking technique that employs Singular Value Decomposition (SVD) algorithm for data protection and authentication.
This paper proposes an adaptive watermark embedding algorithm in the discrete cosine transform (DCT) and singular value decomposition (SVD) domain for raw video. The algorithm first segments video streams into scenes, transforms video frames into DCT-SVD domain, and quantizes watermark embedding on maximum singular values to achieve blind watermark detection. Experimental results demonstrate that the algorithm maintains transparency while resisting various attacks including low-pass filtering, median filtering, salt-and-pepper noise, Gaussian noise, H.264 compression, statistical attacks, and frame cropping.
A MATLAB program for digital watermarking using Singular Value Decomposition (SVD) and basic Arnold scrambling method. This implementation intentionally excludes wavelet algorithms. The uploaded dwt_svd_GUI.rar contains a complete digital watermarking system employing chaotic encryption for enhanced security.
MATLAB implementation of SVD-based feature extraction for face recognition systems with optimization considerations
SVD-Based Sea Clutter Suppression Algorithm - Includes source code implementation with signal processing functions and decomposition methods
Implementing SVD-based noise reduction for Lorenz signals through matrix decomposition to extract principal signal features and eliminate noise components.