Explore MATLAB source code curated for "反变换" with clean implementations, documentation, and examples.
This MATLAB-based program performs TP transforms and inverse transforms on wavefields to achieve wavefield separation. The implementation includes both forward and inverse transformation algorithms and comes with a sample theoretical seismic record for demonstration purposes.
Short-Time Fourier Transform and Inverse Transform - Absolutely Executable with Practical Code Implementation!
Robust Controller Design Using RBF Networks - This approach leverages Radial Basis Function (RBF) networks to approximate arbitrary nonlinear relationships. The objective is to minimize the sum of squared errors, aligning with nonlinear Principal Component Analysis (PCA) goals. The nonlinear PCA model can be implemented using two separate RBF networks: one for nonlinear forward transformation and another for inverse transformation. Each RBF network is a three-layer feedforward architecture with radial basis functions as activation functions in the hidden layer. The first network maps high-dimensional data to a low-dimensional space (Figure 4), while the second network reconstructs the original high-dimensional data from the low-dimensional representation (Figure 5). Both networks require independent training to ensure optimal performance.
Classification of Polar Coordinate, Log-Polar Transformations and Their Inverse Operations: MATLAB and Image Processing Applications
For an input two-dimensional grayscale image, this process first applies the lifting Haar wavelet transform, then compresses the wavelet coefficients using the classic EZW algorithm, and finally reconstructs the original image through inverse transformation. The implementation involves key steps including wavelet decomposition, coefficient quantization, and hierarchical encoding for efficient compression.
This implementation performs both forward and inverse Arnold transformations, which are essential for image encryption, decryption, and compression applications. The code demonstrates chaotic pixel shuffling for security purposes and reversible reconstruction of original images.
Fourier transform and inverse transform operations in MATLAB environment
Comprehensive MATLAB implementation of Radon transform algorithms, including both forward Radon transformation and inverse Radon transform programs with detailed code explanations and parameter specifications.
A MATLAB-based implementation demonstrating Fourier transform and inverse transform operations with signal processing applications.
Discrete Hankel transform (DHT) and inverse transform of integer order n, including fast Hankel transform (FHT) algorithms for efficient forward and inverse computations.