Explore MATLAB source code curated for "灰度图像" with clean implementations, documentation, and examples.
This package contains bit plane decomposition of an image, demonstrating how a grayscale image can be separated into eight bit planes. This technique is particularly useful for converting grayscale images to color images through algorithmic processing.
Grayscale expansion and grayscale image pseudocolorization: Pixels with grayscale values between 0-20 are displayed in blue, 20-40 in green, and 40-255 in red. This technique enhances visual distinction through color mapping algorithms.
Converting grayscale images to pseudocolor images using MATLAB. This simple program demonstrates how to transform grayscale images into pseudocolor versions by implementing color mapping algorithms. By adjusting conditional parameters and value mappings in the code, you can create more refined pseudocolor images with enhanced visual representation.
Performing read/write operations on color images with transformations to obtain various image types such as grayscale images, including code implementation approaches and key functions.
MATLAB implementation of grayscale image threshold segmentation using the two-dimensional Otsu method with detailed algorithm explanation and code structure
MATLAB source code implementation for two-dimensional Otsu automatic threshold segmentation of grayscale images
Implementation of horizontal, vertical, and custom diagonal projections for grayscale images, with the diagonal projection being an original algorithm that has been thoroughly tested and validated.
This source code implements Curvelet feature extraction from both color and grayscale images, designed for subsequent texture image segmentation tasks. The implementation includes multi-scale directional analysis using Fast Discrete Curvelet Transform (FDCT) via wrapping.
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.
Implementing grayscale image compression through DCT transformation, calculating compression ratios, and comparing images with different compression levels using MATLAB-based algorithms.