Explore MATLAB source code curated for "直方图" with clean implementations, documentation, and examples.
Processing a given image by generating its histogram, implementing linear transformation for grayscale stretching to enhance contrast, and performing histogram equalization for improved brightness distribution.
Histograms and histogram equalization are techniques used to enhance local contrast in many images, particularly when the useful data in an image has very similar contrast levels. By redistributing brightness values across the histogram, this method improves local contrast without affecting overall contrast. Histogram equalization achieves this by effectively expanding the most frequent intensity ranges. In implementation, this typically involves calculating cumulative distribution functions and mapping original pixel values to new values for optimal dynamic range utilization.
An effective image scrambling algorithm utilizing bitwise XOR operations between adjacent pixels, capable of significantly altering the image histogram distribution.
Implementation of foreground-background segmentation through histogram-based adaptive thresholding method with code integration
Source code for Boundary Direction Histogram algorithm, demonstrating significant utility in image shape extraction with gradient orientation analysis
Digital image processing experiment using histogram analysis to determine segmentation threshold at approximately 130, converting grayscale images to binary with optimal segmentation results. Image Processing Experiment (Part 6) - Image Compression and Encoding: Learning JPEG compression encoding through three key steps: DCT transformation for redundancy removal, quantization using quantization tables, and Huffman encoding of quantized coefficients.
Implementation of local image selection, camera activation, photo capture, image saving, histogram generation, image inversion, horizontal mirroring, and vertical mirroring with detailed code architecture.
A simple MATLAB application for opening images through a graphical interface and displaying their histograms, developed using MATLAB's image processing capabilities.
This implementation provides three histogram-related algorithms: Histogram Equalization, Histogram Specification, and Contrast Limited Adaptive Histogram Equalization (CLAHE). Each algorithm is organized in separate directories containing corresponding test files that demonstrate their functionality through executable demonstrations.
The LBP function computes local binary pattern images or LBP histograms from input images, providing texture features for computer vision applications with configurable neighborhood parameters.