MATLAB Implementation of Image Fusion Techniques
Comprehensive image fusion approaches including high-pass filtering, IHS transformation, principal component analysis, wavelet-based methods, and combined wavelet-IHS methodology.
Image Sharpening with 5 Distinct Gradient Enhancement Methods
Implementation of 5 gradient-based enhancement techniques for image sharpening, high-pass filtering with mask processing, noise reduction using Butterworth low-pass filters, and image sharpening through Butterworth high-pass filters. Includes practical code implementations for each method.
Second-Order Butterworth High-Pass Filter Implementation with Code Integration
Implementation of second-order Butterworth high-pass filtering with result visualization. After extraction, place both the image and program in the same directory, then execute the filtering algorithm to process digital images.
Fast Fourier Transform (FFT) Filtering
Fast Fourier Transform (FFT) Filtering with capabilities for high-pass, low-pass, and band-pass filtering operations.
MATLAB Filtering Program with Spatial and Frequency Domain Processing
This MATLAB filtering program implements spatial domain 2D convolution filtering, frequency domain high-pass filtering, and third-order Butterworth high-pass filter design. The program demonstrates excellent performance and includes comprehensive code comments, ensuring strong generalizability and portability for signal processing applications.
Multimethod Panchromatic and Multispectral Image Fusion
Implementation of multiple techniques for panchromatic and multispectral image fusion: (1) IHS Transform, (2) High-Pass Filtering, (3) GIHS Method, (4) Wavelet Transform, (5) PCA, and (6) Brovey Transform, including algorithm explanations and code implementation insights.
Applying Low-pass and High-pass Filters to the Original Signal
In the one-level wavelet decomposition process, the original signal undergoes low-pass and high-pass filtering respectively, followed by binary downsampling to obtain low-frequency and high-frequency coefficients (also referred to as approximation and detail coefficients). Multi-level decomposition recursively applies the same wavelet decomposition to the low-frequency coefficients obtained from the previous level, enabling hierarchical signal analysis.
Noise Addition and Filtering Processing on Speech Signals
This implementation performs noise addition to speech signals followed by processing using low-pass, band-pass, and high-pass filters. The program runs perfectly, but requires careful attention to audio file path configuration to avoid runtime errors! Includes code descriptions for signal processing workflows and filter implementations.
High-Pass Filter Implementation and Usage
Comprehensive guide to writing and applying high-pass filters, including instructions for adapting the code to your specific image file paths - users must modify the source image paths according to their local directory structure.