Explore MATLAB source code curated for "改进" with clean implementations, documentation, and examples.
This image correction program, developed in MATLAB environment, is a custom implementation that may require further refinement. The code demonstrates fundamental image processing techniques including geometric transformation and distortion correction algorithms.
This MATLAB code implements the Ensemble Empirical Mode Decomposition (EEMD) algorithm developed by Huang in 2009 as an improvement over the original Empirical Mode Decomposition (EMD) method, featuring signal processing enhancements and noise-assisted data analysis capabilities.
This paper presents an improvement to the widely-used nearest neighbor method in SLAM data association. Our approach replaces the computationally expensive Mahalanobis distance calculations between all features and measurements with Euclidean distance computations between feature estimated positions and vehicle predicted positions. This modification eliminates numerous matrix multiplication operations, resulting in a simpler algorithm implementation that significantly reduces computational complexity. The proposed method maintains comparable association performance to global nearest neighbor approaches while facilitating real-time execution of SLAM algorithms.
My custom implementation of the Poisson image blending algorithm with areas for potential optimization and enhancement. This implementation includes the core gradient-domain operations and demonstrates the mathematical foundation of Poisson equations in image processing.
iNEDI stands for "improved New Edge-Directed Interpolation" - an enhanced image interpolation technique that builds upon the original NEDI method described in X. Li et al.'s seminal paper "New Edge-Directed Interpolation" published in IEEE Transactions on Image Processing, Vol. 10, No. 10, October 2001
Implementation of Particle Swarm Optimization as an enhancement operator for Genetic Algorithms, with code-level integration examples for educational purposes
The Non-Local Means algorithm is an adaptive spatial filtering technique, considered an improvement over Yaroslavsky filtering and bilateral filtering algorithms, with enhanced noise reduction capabilities through weighted similarity comparisons.
An improved image segmentation approach built upon single-layer fuzzy image enhancement algorithms, incorporating advanced computational techniques for better performance
A particle filter improved using an error correction algorithm that primarily addresses the issue of local filtering divergence in particle filtering systems
Enhancement and Simulation of LEACH Protocol's Cluster Head Selection Algorithm - The improved LEACH-H protocol incorporates residual energy considerations during cluster head election by setting energy thresholds to prevent low-energy nodes from becoming cluster heads. It introduces a cluster head adjustment process that eliminates tightly neighboring cluster heads and adds necessary ones, addressing LEACH protocol limitations to balance network energy consumption and extend network lifetime. Network simulations validate the algorithm's feasibility.