Explore MATLAB source code curated for "迭代算法" with clean implementations, documentation, and examples.
This MATLAB source code implements a blind deconvolution iterative algorithm for image restoration, which provides significant benefits for digital image processing education and practical implementation with built-in algorithmic demonstrations.
MATLAB-based medical image segmentation algorithm featuring iterative optimal threshold selection, suitable for beginners with included visualization graphs and detailed code documentation.
Programming implementation of general channel capacity iterative algorithm using MATLAB software with code optimization and performance analysis
Adaboost source code - Adaboost is an iterative algorithm that trains multiple classifiers (weak learners) on the same training dataset and combines them to form a more powerful final classifier (strong learner). The algorithm implementation typically involves weighted training instances and sequential classifier training with error-based weight adjustments.
Landweber iterative algorithm, an iterative class algorithm used for ECT (Electrical Capacitance Tomography) image reconstruction with implementation insights
Application Background: This algorithm implements an iterative procedure for solving large sparse systems of equations, demonstrating good convergence properties. While iteration time becomes longer for extremely large systems - a notable limitation - the method significantly improves computational efficiency for solving linear equations and delivers accurate solutions. Key Technology: The SiRT algorithm provides an efficient iterative approach for large sparse linear systems with robust convergence characteristics. Although computational time increases with system size, it remains a practical tool that produces reliable numerical solutions while enhancing overall solving capabilities.
This function implements an iterative algorithm to calculate channel capacity using Blahut-Arimoto approach. The main function [CC, Paa] = ChannelCap(P, k) computes optimal channel capacity where P represents the forward transition probability matrix and k specifies the iteration precision. Key variables include: CC (optimal channel capacity), Paa (optimal input probability matrix), Pa (initial input probability), Pba (forward transition matrix), Pb (output probability matrix), C (initial capacity), r (number of input symbols), and s (number of output symbols).
CT reconstruction algorithms featuring iterative ARTII and multiplicative ART methods, with emphasis on initialization requirements - the initial value selection cannot be zero to ensure algorithm convergence and effectiveness.
An iterative algorithm for calculating arbitrary channel capacity, featuring convenient implementation and practical utility in communication systems.
1. Select an image and apply blurring using either defocus model or motion blur model; restore the blurred image using inverse filtering, displaying the original, blurred, and restored images. Implementation involves generating point spread functions (PSFs) and applying convolution operations. 2. Perform iterative deconvolution restoration on the blurred image using algorithms like Lucy-Richardson or Wiener filtering, displaying all three image stages. The iterative approach typically involves regularization techniques to handle noise amplification.