Explore MATLAB source code curated for "图像处理" with clean implementations, documentation, and examples.
Technical implementation of ROMP algorithm for compressed sensing applications in image processing, featuring MATLAB/Python code demonstrations and signal reconstruction methodologies.
Implementation of Gaussian filtering on images with comparison display of original and processed results, including code-level insights
This MATLAB-based program implements image skeletonization functionality with efficient processing capabilities for rapid image transformation.
MATLAB-based implementation of Gaussian Mixture Model algorithm for advanced image processing applications including image enhancement, edge detection, and segmentation
Histogram equalization is an image processing technique that redistributes pixel intensities to transform an image's histogram from a concentrated range to a uniform distribution across the entire grayscale spectrum. This non-linear stretching operation enhances contrast by making pixel counts approximately equal within intensity ranges. While effective for improving local contrast, it may reduce grayscale levels and cause unnatural over-enhancement in high-peak histograms. Implementation typically involves calculating cumulative distribution functions and mapping original intensities to new values.
Hough Transform is a fundamental method in image processing. This file provides a MATLAB implementation of Hough Transform developed without using MATLAB's built-in functions, making it ideal for learning and mastering Hough Transform algorithms from scratch.
A comprehensive image processing program developed in MATLAB featuring: P0801 - Call Number Text Image Segmentation, P0802 - Connected Character Segmentation, P0803 - Text Recognition, P0804 - Color License Plate Segmentation, P0805 - Trademark Text Segmentation, with auxiliary functions including: Recognition - text recognition subfunction, StrDetect01 - structural feature extraction subfunction for text recognition
Image acquisition from camera devices on MATLAB platform with comprehensive image processing capabilities, featuring implementation details of Image Acquisition Toolbox functions and image processing algorithms.
A MATLAB source program implementing optical flow algorithm, widely applicable in image processing, foreground detection, and object tracking - recommended for computer vision applications
Perona and Malik established an anisotropic diffusion equation based on the heat conduction equation and applied it to scale-space image smoothing in image processing. This model performs distinct processing on edge and non-edge regions of images, features simple iterative solving schemes, overcomes major drawbacks of traditional filtering methods, significantly improves image quality, and pioneers new directions for image edge detection and enhancement. The implementation typically involves gradient-based diffusion coefficient calculations and iterative updates using finite difference methods.