Different Types of Color Image Processing Using MATLAB with Source Code

-

Different types of Color Image Processing Using MATLAB with Source Code

Digital Image Processing Using MATLAB

Why use color in image processing?
Color is a powerful descriptor.
Object identification and extraction
Example: Face detection using skin colors

Humans can discern thousands of color shades and intensities
Human discern only two dozen shades of grays

Types of color image processing
Pseudo-color processing
Full color processing

Full color processing
Images are acquired from full-color sensor or equipment
Pseudo-color processing
In the past period, color sensors and processing hardware are not available
Colors are assigned to a range of monochrome intensities

Color fundamentals
Color models
Pseudo-color image processing
Color transformations
Smoothing and sharpening

COLOR FUNDAMENTALS
Physical phenomenon
Physical nature of color is identified

Psysio-psychological phenomenon
How human brain see and understand color?

Visible light
Chromatic light distance the electromagnetic spectrum (EM) from 400 to 700 nm

The color that human perceive in an object = the light reflected from the object

Radiance:
Total amount of energy that flow from the light source, measured in watts (W)

Luminance:
Amount of energy an observer perceives from a light source, measured in lumens far infrared light: high radiance, but 0 luminance

Brightness:
Subjective descriptor that is hard to measure, alike to the achromatic view of intensity

6~7M Cones are the sensors in the eye
3 principal sensing categories in eyes
Red light 65%, green light 33%, and blue light 2%

In 1931, CIE (International Commission on Illumination) defines specific wavelength values to the

Primary colors
B = 435.8 nm, G = 546.1 nm, R = 700 nm
However, we know that no single color may be called red, green, or blue
Secondary colors:
G+B=Cyan,
R+G=Yellow,
R+B=Magenta









Color TV

COLOR MODELS

Color model, color space, color system
Specify colors in a standard way
A coordinate system that each color is represented by a single point

RGB color model

Pixel depth:
The number of bits used to represent each pixel in RGB space
Full-color image:
24-bit RGB color image
          (R, G, B) = (8 bits, 8 bits, 8 bits)





CMY model (+Black = CMYK)
CMY: secondary colors of light, or primary colors of pigments
Used to produce hardcopy output

HSI color model
Will you describe a color using its R, G, B components?
Human describe a color by its hue, saturation, and brightness
Hue : color attribute
Saturation: purity of color (white->0, primary color->1)
Brightness: achromatic notion of intensity

Fig. RGB -> HSI model

Pseudo-color image processing
Assign colors to gray values based on a specified criterion
For human visualization and understanding of gray-scale events
Intensity slicing
Gray level to color conversions

Application 1

Application 2

Application 3

Gray level to color transformation
Intensity slicing: piecewise linear transformation

General Gray level to color transformation

Color transformation
Similar to gray scale transformation
g(x,y)=T[f(x,y)]
Color transformation

RGB (CMY (K) ( HSI
Theoretically, any transformation can be performed in any color model
Practically, some operations are better suitable to specific color model
Example:
g(x,y)=k f(x,y),  0<k<1
HSI color space
Intensity: s3 = k r3
Note: transform to HSI requires complex operations
RGB color space
For each R,G,B component: si = k ri

CMY color space
For each C,M,Y component:
si = k ri +(1-k)

Color image smoothing
Neighborhood processing

MATLAB Source Code For Color Image Processing 

clc;
clear all;
close all;
a=imread('peppers.png');
subplot(3,3,1);
imshow(a);
title('original image');
g=rgb2gray(a);
subplot(3,3,2);
imshow(g);
title('gray conversion');
ntsc_img=rgb2ntsc(a);
subplot(3,3,3);
imshow(ntsc_img);
title('ntsc colour space conversion');
ycbcr_img=rgb2ycbcr(a);
subplot(3,3,4);
imshow(ycbcr_img);
title('ycbcr colour space  conversion');
hsv_img=rgb2hsv(a);
subplot(3,3,5);
imshow(hsv_img);
title('hsv  colour space conversion');
cmy_img=imcomplement(a);
subplot(3,3,6);
imshow(cmy_img);
title('cmy colour  space conversion');
rgb=im2double(a);
r=rgb(:,:,1);
g=rgb(:,:,2);
b=rgb(:,:,3);
num=.5*((r-g)+(r-b));
den=sqrt(r-g).^2+(r-g).*(g-b);
theta=acos(num./(den +eps));
H=theta;
H(b>g)=2*pi-H(b>g);
H=H/(2*pi);
num=min(min(r,g),b);
den=r+g+b;
den(den==0)=eps;
S=1-3.*num./den;
H(S==0)=0;
I=(r+g+b)/3;
hsi=cat(3,H,S,I);
subplot(3,3,7);
imshow(hsi);
title('HSI');

figure(2);
subplot(2,3,1);
imshow(r);
title('red component');
subplot(2,3,2);
imshow(g);
title('green component');
subplot(2,3,3);
imshow(b);
title('blue component');
subplot(2,3,4);
imshow(H);
title('hue component');
subplot(2,3,5);
imshow(S);
title('saturation component');
subplot(2,3,6);
imshow(I);
title('brightness component');

OUTPUT


MATLAB PROGRAM USING INDIVIDUAL FIGURE

clc;
clear all;
close all;
a=imread('peppers.png');
figure(1);
imshow(a);
title('original image');
g=rgb2gray(a);
figure(2);
imshow(g);
title('gray conversion');
ntsc_img=rgb2ntsc(a);
figure(3);
imshow(ntsc_img);
title('ntsc colour space conversion');
ycbcr_img=rgb2ycbcr(a);
figure(4);
imshow(ycbcr_img);
title('ycbcr colour space  conversion');
hsv_img=rgb2hsv(a);
figure(5);
imshow(hsv_img);
title('hsv  colour space conversion');
cmy_img=imcomplement(a);
figure(6);
imshow(cmy_img);
title('cmy colour  space conversion');
rgb=im2double(a);
r=rgb(:,:,1);
g=rgb(:,:,2);
b=rgb(:,:,3);
num=.5*((r-g)+(r-b));
den=sqrt(r-g).^2+(r-g).*(g-b);
theta=acos(num./(den +eps));
H=theta;
H(b>g)=2*pi-H(b>g);
H=H/(2*pi);
num=min(min(r,g),b);
den=r+g+b;
den(den==0)=eps;
S=1-3.*num./den;
H(S==0)=0;
I=(r+g+b)/3;
hsi=cat(3,H,S,I);
figure(7);
imshow(hsi);
title('HSI');

figure(8);
subplot(2,3,1);
imshow(r);
title('red component');
figure(9);
imshow(g);
title('green component');
figure(10);
imshow(b);
title('blue component');
figure(11);
imshow(H);
title('hue component');
figure(12);
imshow(S);
title('saturation component');
figure(13);
imshow(I);
title('brightness component');

OUTPUT



Comments

Post a Comment

Popular posts from this blog

Spatial Intensity Resolution Project Using MATLAB with Source Code

-
Spatial Intensity Resolution of an image Using MATLAB with Source Code INTRODUCTION Sampling is the principal factor Estimation of the spatial resolution of an image. Commonly spatial resolution is the smallest perceptible detail in an image, a widely used meaning of resolution is simply the smallest number of discernible line pairs per unit distance ; for estimation 100 line pairs/mm. Gray level resolution: This refers to the smallest visible change in gray level. The measurement of visible changes in gray level is a extremely subjective procedure. We have significant discretion concerning the number of Samples used to generate a digital image. But this is not true for the amount of gray levels. Due to hardware restraints, the number of gray levels is usually an integer power of two. The most common value is 8 bits. It can vary depending on application. When an actual portion of physical resolution relating pixels and level of detail they resolution in the original scene are not n
Read more

Image Intensity Transformation Using MATLAB with Source Code

-
Intensity Transformation Using MATLAB Theoretical Concepts: Introduction What is Intensity Transformation? Intensity transformation is increase the contrast between certain Intensity values, most important application of intensity transformation is Enhance the low Quality image. Image Enhancement Techniques Spatial operates on Pixels Frequency domain operates on Fourier transform of image Spatial Domain Methods Spatial Domain Technique a operation (linear or non-linear) is performed on the pixels in the neighborhood of coordinate (x,y) in the input image F, giving enhanced image F’ Neighborhood can be any shape but generally it is rectangular ( 3x3, 5x5, 9x9) g(x,y) = T[f(x,y)] Grey Scale Manipulation Simplest form of window (1x1) Assume input gray scale values are in range [0, L-1] (in 8 bit images L = 256) Nth root Transformation S = c (r)n . Intensity Transformation function Linear: Negative Identity Logarithmic:  Log Inverse Log Power-Law:  nth power, nth
Read more

Histogram Equalization Project Using MATLAB with Source Code

-
Histogram Equalization Introduction: This application note defines a technique of Digital imaging processing which allows medical images to have better contrast. This is achieved via the histogram of the image, making use of a technique that allows the places with minimal contrast to improvement increasing contrast by circulation out the most regular intensity values. For example, in digital x-rays in what kind of colors achieved are a palette of whites and blacks, unlike types of colors give the physician an idea of the type of density that he or she is perceiving. Therefore white components are most likely to indicate bone or water and black structures mean air. Whenever pathologies are existing in an image, attempting to define the region of the lesion or object of interest may be a challenge, because a variety of structures are usually layered one over the other. like in the case of the chest the heart, lungs, and blood vessels are so near with each other that contrast is critic
Read more