Explore MATLAB source code curated for "图像处理" with clean implementations, documentation, and examples.
A snake algorithm implementation for contour tracking that can be applied in image processing fields such as image segmentation and object recognition, featuring dynamic curve evolution through energy minimization.
This MATLAB-based implementation includes sample images and is ready to run (requires path modification). Designed for crack detection in bridge decks and road surfaces, the code features detailed function descriptions and algorithmic explanations. Key implementations include frequency estimation of regular stripe patterns in road backgrounds and frequency-domain filtering to effectively remove noise near estimated frequencies while preserving target cracks. The current version provides basic detection functionality with room for improvement, serving as a demonstrative prototype. Interested developers are welcome to enhance and adapt this codebase - please contact me for collaboration opportunities.
MATLAB Code Implementation of Independent Component Analysis for Image Processing Techniques
Several MATLAB-based image processing utilities focusing on PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error) calculations, designed to assist researchers in image quality assessment and compression analysis.
This collection features 30 intelligent algorithm programs and analyses specifically designed for MATLAB image processing applications. Each program includes complete working code examples with detailed implementation approaches, algorithm breakdowns, and key MATLAB function explanations - making it ideal for beginners learning image processing techniques through practical coding examples.
1. Select an image and perform DCT block operations (dividing image into 8×8 blocks, applying DCT transform to each block). 2. Use masking matrix for DCT coefficient region selection, perform inverse DCT to reconstruct original image, and calculate PSNR of synthesized image. 3. Choose a wavelet function to decompose an image, display transform coefficients to observe patterns, and perform inverse wavelet transform for image reconstruction.
This MATLAB M-file implements Wiener filtering primarily for signal and image processing applications, featuring two practical implementation examples with code-level demonstrations of noise reduction and enhancement techniques.
Comprehensive MATLAB image processing routines including skeletonization, grayscale image gradient computation, convex hull extraction, and image thinning/thickening operations with algorithmic implementations.
MATLAB implementation for pattern recognition in image processing utilizing genetic algorithm optimization, featuring image preprocessing, feature extraction, and performance evaluation modules.
This is my MATLAB implementation of an adaptive histogram equalization algorithm designed to enhance image details, combining the advantages of detail-enhancing adaptive histograms and traditional histogram equalization techniques. The program includes comprehensive algorithm explanation and demonstrates practical implementation using MATLAB's image processing toolbox functions like adapthisteq() with customized parameter settings for optimal contrast enhancement.