Explore MATLAB source code curated for "彩色图像" with clean implementations, documentation, and examples.
Performing histogram equalization on acquired color images enables uniform distribution of luminance levels across the image, typically implemented through separate channel processing or color space conversion approaches.
An advanced color image edge detection algorithm with superior detection performance, improved from the Canny algorithm, featuring precise localization, high response standards, high detection rate, and low false positive rate, with implementation insights including gradient computation in RGB/HSV color spaces and multi-channel fusion techniques.
MATLAB source code implementation for the paper "Color Image Edge Detection Based on Quaternion Convolution" - including quaternion convolution operations, color space processing, and edge detection algorithms
Implementing seamless fusion of two color images using MATLAB, with the output result being a grayscale image. Key steps include image loading, alignment, feature extraction, fusion algorithms, and grayscale conversion.
A color image encryption algorithm based on Fractional Fourier Transform, primarily designed for digital image security applications with implementation insights.
This program utilizes singular value decomposition (SVD) to compress and decompose 3-channel color images through the following technical steps: Compression Process: 1. Select sub-image size K to decompose the image into M×M sub-images (IMG(s), s=1,2,...,M², where M=N/K, original image size N×N). 2. Calculate average of M² sub-images and subtract mean from each sub-image. 3. Compute correlation matrix R with elements defined by covariance relationships. 4. Calculate eigenvalues/eigenvectors of R, then obtain compressed encoding via dot products between sub-images and principal eigenvectors. Implementation features eigenvalue-based dimension reduction for efficient color channel processing.
Conversion of color images from RGB color mode to HSI color model with algorithm implementation details
To prevent color distortion when directly processing RGB images, color images are typically converted from RGB space to HSV space for processing. After completing the corresponding operations, the image is inversely transformed back to RGB space. This conversion simplifies color manipulation by separating hue, saturation, and brightness components, with implementation typically involving color space transformation algorithms using functions like rgb2hsv() in image processing libraries.
Denoising of grayscale images, color images, and video sequences using the advanced BM3D (Block-Matching 3D) method, featuring collaborative filtering and hard/soft thresholding techniques.
Perform differential operations on color images with RGB and luminance histogram analysis, identifying zero second-derivative points through custom algorithm implementation achieving optimal results