Finger Vein Image Processing Algorithm
This code processes finger vein images through a comprehensive pipeline including denoising, cropping, Niblack thresholding, median filtering, and thinning to produce refined and noise-reduced vein patterns.
MATLAB Implementation of Image Median Filtering with Performance Analysis
Comparative analysis of median filtering and low-pass filtering techniques for image denoising, including PSNR evaluation to quantify performance differences in MATLAB
Traditional Methods in Image Processing - Median Filtering
Traditional Method in Image Processing: Median Filtering - A Comprehensive Guide
Image Denoising Techniques and Implementation
Image Denoising: Implement various noise types (random pixel noise, salt-and-pepper noise) and compare denoising methods including neighborhood averaging, median filtering, and image stacking with algorithmic analysis and code implementation approaches.
Image Recognition with Preprocessing and Sharpening Techniques
Implementation of grayscale conversion, histogram equalization, and median filtering for image preprocessing, enabling identification of landmark objects in images. Various sharpening methods are explored and compared, providing valuable learning resources for beginners in computer vision.
Fundamental Concept of the Fast Median Filtering Algorithm Proposed in This Paper
The core principle of the fast median filtering algorithm introduced in this paper is that during the sliding window movement process on the original data sequence, the current window only needs to remove its earliest element and incorporate the new element following the window to form the content of the next window. This implementation corresponds to pre-packaged algorithm code in MATLAB, designed for optimized computational efficiency.
SAR Image Filters: GAMMA, Lee, and Median Filtering Techniques
Overview of SAR image filters including GAMMA, Lee, and Median filters with implementation considerations
Image Smoothing and Sharpening Techniques
This package provides implementations for image smoothing and sharpening, featuring multiple methods including mean filtering, median filtering, order-statistic filtering for smoothing, and Sobel operator along with high-pass filtering for sharpening, with detailed code explanations for each algorithm.
MATLAB Image Processing Implementation
Image processing techniques including grayscale transformation, median filtering, binarization processing, edge detection, and one-dimensional size measurement with MATLAB implementation approaches
Image Filtering and Denoising with Median Filter Implementation
Implement median filtering for image denoising with noise addition capabilities for comparative analysis using MATLAB's image processing functions