Explore MATLAB source code curated for "图像去噪" with clean implementations, documentation, and examples.
This code implements a dual-density dual-tree complex wavelet transform algorithm, providing effective solutions for image denoising and image compression. The source code demonstrates excellent implementation quality with robust signal processing capabilities.
MATLAB implementations of Bayesian and universal threshold soft-thresholding image denoising algorithms with detailed code explanations and wavelet transform applications
Implementation of Markov Random Fields for Image Restoration, including MRF generation algorithms and applications in image denoising with code-level implementation details
MATLAB-based fixed-point iteration method for image denoising, implementing a partial differential equation algorithm with adaptive parameter tuning and multi-scale processing.
MATLAB implementation of image denoising using fractional B-spline wavelets with algorithmic explanations and code structure details.
A MATLAB-based program for image denoising, designed to be simple and easy to understand! Incorporates noise reduction algorithms with clear implementation examples.
This MATLAB-based hidden Markov tree model implementation computes Contourlet coefficients for images, incorporating application models for image denoising and texture restoration. The code includes comprehensive functionality for multiscale image decomposition and statistical modeling of transform coefficients.
Image Preprocessing - Denoising Filters - MATLAB Implementation with Detailed Code Examples and Algorithm Explanations
Implementation of TIP10 paper "Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization" featuring an advanced algorithm for image restoration with code-level insights
Utilizing wavelet transform for image denoising by applying thresholding techniques. The process involves a 2-level wavelet decomposition of the image followed by hard and soft thresholding methods to remove noise from high-frequency components. Implementation typically involves using wavelet functions like 'db4' or 'sym8' and threshold calculation methods such as Universal Threshold or SURE threshold.