MATLAB Source Code for Color Image Bilateral Filter Simulation
MATLAB implementation of bilateral filter simulation for color images, featuring edge-preserving noise reduction with customizable parameter tuning.
Removing Shadows from Color Images for Enhanced Computer Vision
Techniques for eliminating shadows in color images to facilitate image segmentation, object recognition, and tracking operations with code implementation insights
Image Recognition and Matching: Color Image Matching with Code Implementation
Image Recognition and Matching for Color Images with High Accuracy Rate and Fast Processing Speed, featuring algorithm explanations and key function descriptions
MATLAB Implementation of Bilateral Filtering for Edge-Preserving Smoothing
This program implements Bilateral filtering for edge-preserving smoothing, suitable for both grayscale and color images. The implementation handles spatial and intensity domain weighting with configurable parameters.
Histogram Matching for Color Images with MATLAB Implementation
Implementation of color image histogram matching using MATLAB, including complete source code and comprehensive documentation with algorithmic explanations
Histogram Equalization of Color Images in RGB and HSV Color Spaces
Performing histogram equalization on input color images in both RGB and HSV color spaces, with comparative display of equalization effects including code implementation approaches
Color Image Chaos Scrambling Encryption Algorithm
Absolutely accurate color image chaos scrambling encryption algorithm - presenting the best introductory gift with robust code implementation.
Vector Median Filter (VMF) for Color Image Processing
Vector Median Filter (VMF) - A noise reduction algorithm that preserves inter-channel correlations in color images by treating pixel values as vectors and selecting the median vector based on distance metrics.
Pseudo-color Processing: Converting Grayscale Images to Color Images
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.
MATLAB Function for Arbitrary Color Image Rotation
MATLAB function for rotating color images with post-rotation point filling required for grayscale images