Source Code for Gaussian Noise Generation
MATLAB-based source code implementation for generating Gaussian noise with detailed algorithm explanations
Effective Source Code for Gaussian Noise Removal
High-performance Gaussian noise removal source code with superior denoising capabilities and straightforward implementation
Smoothing and Sharpening (Edge Detection) in Digital Image Processing
Smoothing and Sharpening (Edge Detection) in Digital Image Processing. Includes: 1. Adding salt-and-pepper and Gaussian noise. 2. Smoothing noise-contaminated images using neighborhood averaging, median filtering, and K-nearest neighbor averaging methods. 3. Sharpening images using Roberts gradient, Sobel operator, and Laplacian operator methods with comparative result analysis. Accompanied by source image and processed result screenshots with implementation code insights.
Adding Gaussian Noise to Speckle Patterns
Implementation of Gaussian noise addition to generated speckle patterns for enhanced complexity and realism
Effectiveness of Mean Filtering on Gaussian Noise with Code Implementation
Mean filtering performance on Gaussian noise, 2D adaptive Wiener filtering effectiveness for Gaussian noise removal, comparative analysis of mean/median/Wiener filters on salt-and-pepper noise, 2D statistical filtering applications for both noise types, image denoising using wrcoef2 function with MATLAB implementation examples
Implementation of Sliding Window Detection for Small Targets Under Gaussian Noise
MATLAB implementation of sliding window detection for small targets in Gaussian noise environments. The dataset contains 1000 data points with a window size of 30, shifting one point per iteration. The algorithm squares each of the 30 data points within the window, sums them up, and divides by the window size. Experimental results demonstrate effective radar target signal detection. Key implementation involves using moving window functions and power calculation methods.
MATLAB Gaussian Noise Generation Function for Communication Systems
MATLAB Gaussian Noise Generation Function (for Communications) with Implementation Details
Method for Estimating the 1.5-Dimensional Spectrum of Signals
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.
BER Simulation of APSK under Gaussian Noise with HPA Amplifier Modeling
Implementation of APSK BER simulation under Gaussian noise conditions incorporating HPA amplifier using Saleh model, featuring comprehensive parameter analysis and performance comparisons
Adding Gaussian Noise and Salt-and-Pepper Noise to Grayscale Images with Filtering Implementation
This experiment demonstrates adding Gaussian noise and salt-and-pepper noise to grayscale images, followed by median filtering and mean filtering processing with code implementation details