Explore MATLAB source code curated for "伪彩色处理" with clean implementations, documentation, and examples.
Implementing image pseudocolor processing through gray level transformation and frequency domain filtering methods with MATLAB programming implementation, including algorithm explanations and key function descriptions
Pseudo-color processing refers to the conversion of grayscale (black-and-white) images into color images or the transformation of monochromatic images into images with specified color distributions. Since the human eye can distinguish colors much more effectively than shades of gray, converting grayscale images to color representations improves the ability to detect image details. The fundamental principle involves mapping each grayscale level to a specific point in the color space, enabling the transformation of monochrome images into color images by assigning distinct colors to different gray levels. In code implementation, this typically involves creating a color lookup table (LUT) where grayscale values are mapped to RGB triplets using linear or nonlinear transformation algorithms.
Source code for theoretical foundations of color processing, including grayscale conversion and pseudo-color processing, color adjustment techniques, brightness/contrast modification, color quantization and reduction, dithering algorithms, and enhanced CDib implementations.