Explore MATLAB source code curated for "DWT" with clean implementations, documentation, and examples.
MATLAB-based compressed sensing implementation featuring DCT, DWT, DFT orthogonal bases and overcomplete dictionaries for sparse signal decomposition and reconstruction, including algorithm workflows and key function demonstrations
This MATLAB 7.0 implementation package includes practical programs demonstrating Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT), and FFT Shift operations for signal processing applications.
Image compressive sensing implementation using FFT (constructing orthogonal transformation matrix) or DWT (measuring high-frequency coefficients) as sparsity bases, with Orthogonal Matching Pursuit (OMP) algorithm for reconstruction
Implementation of 2D wavelet transform using Discrete Wavelet Transform (DWT) with practical code applications demonstrates excellent functionality and versatility
High-Capacity Wavelet Watermarking Implementation Extract all files into MATLAB's current directory and type "test_main" in the command window to execute the main watermarking algorithm
Comparing the features of DCT and DWT in digital image watermarking applications with algorithm implementation insights and performance analysis.
I have utilized the DWT wavelet transform source code for discrete wavelet transformation with satisfactory performance. The implementation demonstrates effective signal processing capabilities through multi-resolution analysis.
Image compression implementation using DWT and DCT algorithms with code-level insights into frequency domain transformation techniques
The image undergoes first-level DWT decomposition, where two uncorrelated pseudo-random sequences represent 0 and 1 in the watermark information, embedded into the LH and HL subbands of the wavelet decomposition. Note: The complete algorithm consists of two parts: watermark embedding and watermark extraction.
This demonstration program implements 2D wavelet transform using Discrete Wavelet Transform (DWT) with key steps: 1. Loading original image matrix 2. Performing 2D wavelet decomposition 3. Displaying decomposition results and reconstructed source image with visualization