Explore MATLAB source code curated for "直方图均衡化" with clean implementations, documentation, and examples.
Implementing a comprehensive image processing pipeline for pavement crack detection: converting images to grayscale, applying noise reduction filters, enhancing contrast through histogram equalization, differentiating between linear and mesh cracks, and quantitatively measuring crack length (linear) and area (mesh) for damage assessment.
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.
Chapter 13: MATLAB Image Enhancement - 13.1 Gray-scale Transformation Enhancement (Pixel Values & Statistical Properties, Histogram Gray-scale Transformation, Histogram Equalization, Histogram Specification) - 13.2 Spatial Filtering Enhancement (Smoothing Filters, Sharpening Filters) - 13.3 Frequency Domain Enhancement (Low-pass Filters, High-pass Filters, Homomorphic Filters, Frequency Domain Enhancement MATLAB Examples) - 13.4 Color Enhancement (True Color Enhancement, Pseudo-color Enhancement) - 13.5 Wavelet Enhancement
This program implements histogram equalization to achieve image enhancement effects, which is a crucial component in digital image processing. The code processes image histogram distribution to improve contrast and visual quality.
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.
Utilizing adaptive algorithm MATLAB programs for image processing that overcomes the limitations of histogram equalization through dynamic contrast adjustment techniques
GUI interface design for general image processing operations including rotation, noise addition, histogram equalization, and advanced techniques with MATLAB implementation details
Implementation of contrast enhancement for indexed images using histogram equalization, image adjustment, and adaptive histogram equalization functions, with visualization of contrast improvement effects through comparative image display
MATLAB image preprocessing including grayscale conversion, logarithmic transformation, histogram equalization, linear smoothing filters, median filtering, adaptive filtering, image sharpening, image binarization, various edge detection operators, and their code implementations
Implementation based on Gonzalez's Digital Image Processing methods using MATLAB for histogram equalization and histogram matching procedures, including the original image for comparison