Image Smoothing Processing
Image smoothing effectively removes noise interference, and when combined with sharpening techniques, it can significantly enhance image quality with proper implementation using filters and algorithms.
X-CT Image Processing Using Wavelet Decomposition for Noise Removal
MATLAB implementation for X-CT image processing with wavelet decomposition-based noise removal techniques, including algorithm specifications and function implementations
Objective of Denoising Images Corrupted by Mixed Noise through Algorithm Comparison
Achieving effective denoising for images contaminated by mixed noise through comparative analysis of six methodologies, identifying the optimal denoising algorithm with implementation insights.
Linear Non-Local Means Filter for Noise Removal and Image Smoothing
The Linear Non-Local Means Filter is used in image processing for denoising and smoothing images while effectively preserving edge details through pixel similarity comparisons across the entire image.
Comparison of FFT Algorithms and Low-Pass Filters in Noise Removal Applications
Comparative Analysis of FFT-Based Signal Processing and Low-Pass Filtering Techniques for Effective Noise Reduction
Wavelet Transform MATLAB Implementation
MATLAB source code for wavelet transform implementation, designed for speech enhancement applications with effective noise reduction capabilities. Features straightforward algorithm implementation and clearly structured code.
Interpolation Function
Interpolation Function for Noise Removal in Data Processing
MATLAB Implementation of PCNN Algorithm for Image Noise Removal
A MATLAB-based program implementing Pulse-Coupled Neural Networks (PCNN) for image denoising, featuring customizable parameters and neuroscience-inspired processing