Explore MATLAB source code curated for "数字图像处理" with clean implementations, documentation, and examples.
A highly practical digital photo denoising method implemented in MATLAB, featuring wavelet transform and frequency domain filtering algorithms - valuable for image processing practitioners
MATLAB code implementations for digital image processing, featuring common filters and image processing algorithms with detailed code explanations
A comprehensive PowerPoint tutorial on digital image processing, featuring practical code examples and algorithm explanations to enhance technical understanding
Comprehensive digital image processing implementations including Fourier Transform, Wavelet Transform, Sharpening Algorithms, Histogram Equalization, and more
Huffman coding implementation for digital image processing. This program performs Huffman encoding on input images, displays the original image's entropy and average code length after encoding, and reconstructs images from the encoded data. Built using C++/MATLAB hybrid programming with pre-compiled MATLAB library files, requiring no additional C++ linking. Includes source code, original test images, and encoded result images.
MATLAB-based digital image processing with comprehensive noise addition techniques, including algorithm implementation and noise simulation methods
Digital image processing techniques implemented in MATLAB 6.5, featuring image erosion and dilation operations with detailed explanations, sample images, and result demonstrations. Includes code implementation approaches using morphological operators and structuring elements.
This software's core functionality is divided into two primary modules: Digital Image Processing and Digital Image Matching. The processing module enables users to perform fundamental image manipulations including scaling (zoom in/out), flipping, and grayscale conversion. The matching module allows users to compare two images, display matching results, and supports advanced operations like feature extraction and similarity calculation through robust algorithms such as SIFT or ORB.
Digital image processing using K-SVD dictionary learning method, sparse and redundant representation theory for signals, featuring MATLAB implementation examples for image compression and image denoising applications with algorithm explanations.
MATLAB source code implementation of watershed image segmentation algorithm, a fundamental technique in digital image processing that enables pixel-level image partitioning and object identification through gradient-based region separation.