Explore MATLAB source code curated for "图像去噪" with clean implementations, documentation, and examples.
Implementation of image denoising through contourlet transforms, wavelet transforms, and similar approaches, featuring fully functional code after debugging with excellent performance results
Image denoising represents an essential aspect of image processing, constantly requiring a balance between noise suppression and detail preservation. Wavelet transform has emerged as a powerful signal processing tool with successful applications in noise removal. Traditional orthogonal wavelet-based denoising methods often cause oscillations near edges leading to edge distortion, and can produce blurred edges under severe noise conditions. Redundant wavelet transform techniques overcome these limitations and significantly enhance denoising performance through improved signal representation.
A comprehensive MATLAB program implementing wavelet-based image denoising with excellent noise reduction performance. This implementation demonstrates wavelet decomposition, thresholding techniques, and reconstruction algorithms, making it valuable for students and researchers working on image processing applications.
Image denoising using Orthogonal Matching Pursuit (OMP), with comparative analysis against ITH and BP algorithms through practical implementation and performance metrics
This improved adaptive Canny algorithm utilizes a modified switching median filter for image denoising, partitions the image into multiple sub-images, and adaptively generates dynamic thresholds by comprehensively considering global and local edge gradient information.
This program implements wavelet transform-based image denoising - thoroughly tested and ready for immediate use
Eight classic image denoising methods (including sample images), providing implementation insights to enhance your image processing capabilities!
Image denoising implementation in MATLAB using mathematical morphology with enhanced structural element selection. Unlike traditional single-structure-element approaches that yield suboptimal results, this method utilizes multiple structuring elements (disk, square, line) through dilation and erosion operations. The implementation effectively handles Gaussian, salt-and-pepper, and Wiener noise types while maintaining algorithmic simplicity through morphological opening/closing operations with imopen() and imclose() functions.
Source code for image denoising with accompanying English research paper demonstrating excellent performance, featuring algorithm explanations and implementation details
This source code package provides comprehensive image processing capabilities including image denoising, image inpainting, and PSNR calculation, primarily implemented using total variation methods and other advanced techniques.