Explore MATLAB source code curated for "图像滤波" with clean implementations, documentation, and examples.
This toolbox provides comprehensive functions for implementing nonlinear diffusion techniques on images. These filtering algorithms excel at noise reduction and image simplification for improved segmentation results. Core implementations are based on Perona-Malik's anisotropic diffusion model and extended through Weickert's research, featuring practical code modules for various diffusion methodologies.
Texture generation and segmentation - this example demonstrates texture extraction from different image regions using three distinct filtering functions (entropyfilt, stdfilt, rangefilt) and their implementation approaches
MATLAB source code for image filtering featuring GUI implementation, designed for beginners with user-friendly operation and comprehensive filtering algorithms
A comprehensive image preprocessing program implementing grayscale transformation, image filtering, image restoration, wavelet transform, morphological processing, and shape recognition with detailed code explanations
A MATLAB program designed for image filtering that effectively removes severe salt-and-pepper noise, featuring comprehensive annotations for easy understanding of the implementation logic and algorithmic approach.
MATLAB source codes for partial differential equation methods in image processing, covering curve evolution, image segmentation, image filtering, and image restoration with detailed algorithmic implementations.
Source code for implementing image filtering with Pulse Coupled Neural Network (PCNN), fully tested and operational. The implementation includes key PCNN components such as neuron linking, pulse generation, and iterative filtering algorithms.
MATLAB implementations of image processing techniques using Partial Differential Equation (PDE) approaches, featuring essential algorithms for image filtering, segmentation, interpolation, enhancement, restoration, and numerical solutions of equation systems commonly employed in PDE-based image processing. These programs serve as educational resources for understanding practical applications of PDE methods in computer vision.
This code performs image filtering, top-hat transformation, and center calculation with included sample images for immediate execution. The implementation demonstrates key image processing techniques including noise reduction and feature enhancement.
Images filtered using Gabor wavelet transformation through convolution operations between Gabor wavelets and input images.