Explore MATLAB source code curated for "低频分量" with clean implementations, documentation, and examples.
Applying Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) to an image with analysis of high and low frequency component coefficients, including implementation approaches and key mathematical operations
An indirect method for estimating the 1.5-dimensional spectrum of signals, which effectively suppresses Gaussian noise and enhances low-frequency components. Implementation typically involves higher-order spectral analysis techniques and specialized algorithms for noise reduction.
Implementation of DCT transformation for image processing to separate high-frequency and low-frequency components using frequency domain analysis
Perform wavelet transformation on each image channel to obtain horizontal, vertical, and 45-degree diagonal high-frequency components along with low-frequency components, constructing wavelet pyramids for individual channels. Fuse corresponding feature maps from different pyramid levels of both images using a maximum-value selection principle. Finally, reconstruct the fused image through inverse wavelet transformation based on the combined wavelet coefficients.