Doc _ Finale

8/14/2019 Doc _ Finale

1/12

Pseudo Coloration and Contrast EnhancementVysakh P

Department of EcEAmrita School of Engineering

Amritapuri Campus

Sandeep Nair CDepartment of EcE

Amrita School of EngineeringAmritapuri Campus

ABSTRACTThe paper presents algorithms for two of the essential image

processing techniques used in remote data sensing and medical

image processing namely contrast enhancement and pseudo

coloration.

KeywordsPseudo coloration, false coloring, contrast enhancement,

histogram equalization, intensity slicing.

1. INTRODUCTIONGenerally most of the scientific image accusation tools provide

an image in terms of varying grayscale intensities. This is

primarily because the image sensing transducer in most of such

equipment depends on frequencies outside the human visual

frequency. Specific techniques are applied to modify the

available raw data into grayscale intensities perceivable by the

human eye.

The number of gray levels depends on the amount of detail

required to be conveyed via the image.

The human perception is trained by the vibrant colours around

us in nature. So we easily identify patterns that are representedin colour. Also if the image is too dark (low contrast), we fail

to recognize patterns from it.

Exploiting the fact that many of the grayscale intensities can be

uniquely assigned to certain features in a context, many

algorithms for transforming grayscale images to colour images

have been developed.

2. PSEUDO COLOURINGThe process of assigning a particular colour to a particular

feature in a grayscale image preserving the intensity factor (or

not) so as to easily identify it (and represent it as a high

contrast colour image) is called pseudo colouring (or false

colouring). In order to understand the process, we require thebasic knowledge of digital image storage.

2.1 Digital image storageDigital images fall into two main categories:

Grayscale images

Colour images.

Gray scale images contain the intensity information of the

image in a two dimensional matrix format. The individual

matrix elements represent individual pixels and can be 1 to 32

bit long depending on the number of gray levels that are used

to represent the data. Usually the images are stored in 8-bit

long format resulting in 256 different gray levels (with 0

representing black and 255 representing white). At times they

are also represented in decimal format between 0 and 1

depending on the requirement.

Colour images are represented using three dimensional

matrices with each n x m matrix representing the red, green

and blue intensities separately which are taken together to

result in 256 x 256 x 256 possible colour combinations

(in case of 8-bit pixel notation). Representation of each

individual layer (red, green and blue) is similar to that of the

grayscale images.

2.2 Intensity slicing

The key idea used in the pseudo colouration process is to fix a

intensity slicing plane. The gray scale input image can be

represented as a mesh of pixel location vs. intensity. In 8-bit

image representation , the intensity scale ranges from 0 to 255.

So as to perform intensity slicing we fix an arbitrary plane at

an intensity level cutting the mesh into two halves. This is the

process of intensity slicing.

The second process is to assign a colour to the intensities

above and below the plane. Points lying on the plane are

arbitrarily assigned a colour. This is illustrated in the figurebelow.

2.3 ColourationThe process of colouration involves generating a threedimensional colour pixel matrix from the intensity scale


2/12

(grayscale) vector. This follows the intensity slicing operation

above to generate the final pseudo colored image.

The process involves identifying the RGB components of the

colours with which the intensity slices are to be substituted.

Each intensity component in the input vector is categorized

into a slice and the colour vector corresponding to each

intensity point is generated. In order to preserve the intensityinformation, the pixel value in the 3D result is multiplied with

a value proportional to the intensity in the original image.

The entire process can be summarized as below.

3. CONTRAST ENHANCEMENT

Images obtained from deep space telescope transponders or

medical imaging equipments rely mainly on signals from

outside the human visual spectrum. They are made into

visually perceivable format using variety of high endalgorithms and mathematical manipulations. But they usually

result in a grayscale image with gray levels clustered together

in certain regions (usually the low intensity regions). Same is

the case when camera aperture and shutter speeds are not

correctly set for manual cameras..

The resultant image will be dark and its features are not easily

recognizable by human eye.

In order to generate a high contrast gray scale image from a

low intensity image we employ principles of random variables

and histogram equalization.

3.1 Histogram equalizationThe intensity level information in a gray scale image can be

considered to be a random variable. On plotting the population

density (occurrence rate of each intensity level) as a histogram

we see that the intensity spectrum is clustered together near

some intensities. This is illustrated in Figure 3 below.

The equalization process involves normalizing the histogram

and replacing the intensities in the original image with the

intensities from the normalized curve.

The process of equalization involves finding the Cumulative

distribution of the random histogram. This normalizing curve

is used as the base curve for the transformation.

If r(x) is the normalized histogram of the original image(taking values between 0 and 1),

r(x) =1 [x=0, 1, 2.255].

Now, the transformation curve can be obtained by applying the

transformation equation

S(x) = r(x) [x=0, 1, 2.255].

In the case of the above histogram (Figure 3.), the normalized

histogram would look as in Figure 4 below.

Original

image

(gray

vector)

Intensity

slicing

Slice 1

Slice 2

Slice n

Colourization

Pseudo

coloured

outputimage

(colourvector)

Figure2. Pseudo-colouration process. Figure3. A low contrast image and its histogram

Figure4. Normalized histogram (transformation curve)

(see appendix for full details)


3/12

Now each intensity level in the original image is replaced by a

corresponding transformed intensity level as from the

transformation curve in Figure 4. [ S(x) ].

The histogram of the enhanced image will be evenly

distributed throughout the intensities. (shown in Figure 5).

.

The entire process of histogram equalization is schematically

shown below

4. ACKNOWLEDGMENTS

Our sincere thanks to Renu madam (lecturer Amrita School of

Engineering, Amritapuri campus ) for having provided us full

fledged support and motivation to take up this task andcomplete it successfully.

Our gratitude also extends to all staff members of our college

who directly or indirectly was associated with us throughout

the task.

Above all we thank the Almighty for having provided us with

all support in the forms mentioned above or otherwise.

5. REFERENCES[1] Digital Image Processing by Gonzales and Woods

[2] Fundamentals of image processing by Arun K Jain

[3] Image processing basics by

[4] Vidya [Campus intranet based e-book library]

[5] http://www.Wikipedia.com

Figure5. Enhanced image and its histogram.

(see appendix for full details)

Low

contrastgrayscale

image

Histogramformation

Normalizing

curveformation

Intensitysubstitution

Final high Image

Figure6. Histogram equalization process.


4/12


5/12


6/12


7/12


8/12


9/12

AppendixAppendixAppendixAppendixI

%Programtoenhancecontrastofagrayscaleimageusinghistogramequalizationmethod

% Authors:

% VysakhPandSandeepNairC

% S5ECE

% AmritaSchoolofEngineering,Amritapuri;

%*******************************************************************************

%Readingintheimageandconvertingitto'double'formattoenablecalculations

p=double(imread('washsat.tif'));

%displayingtheimage(imshowtakesintensitylevelsscaledfrom0->1)

figure,imshow(p/255);title('Originalfigure')

n=0; %Variableinit

[m,l]=size(p); %calculatingimagesize

k=m*l;

%TofindthePDFoftheintensityrandomvariables

fori=0:255

tmp=p-i; %makingalllocationsofintensityito0.

n(i+1)=k-length(find(tmp)); %Countingoccurrenceofi;

end %populationdensityofintensityiintheimage.

figure,stem(1:256,n);

title('Originalhistogram'); %Plottingtheoriginalhistogram;

n=n/k; %Normalizingthevalues

sum(n) %TestingifthePDFisValid.

%Remove'o'sfromstemplot(source:originalMatlabHisteqfunction)

hs=gca;

h=get(hs,'children');

delete(findobj(h,'flat','Marker','o'))


10/12

%Tofindthecumulativedistribution(togetthenormalizedcurve)

s=0; %Variableinit

fori=1:256

s(i)=sum(n(1:i)); %s(i)=sumofelementsfromn(1)ton(i)(CDF)end

figure,plot(s); %Plotofthenormalizedcurve

title('NormalisingCurve(transformationFn:)');

%Applyingthetransformationobtainedabove

eqim=p; %Storingimageintemporaryvariablefori=1:m

forj=1:l

eqim(i,j)=s(p(i,j)+1); %Eachintensitylevelinoriginalisreplacedwiththe

corresponding

end %variablein

end %thetransformationmatrix(thematrixindexof

%transformationmatrix

%from1-256storingthetransformedintensities%correspondingtoeachintheoriginal

figure,imshow(eqim);

title('Equalizedimage');

eqim=double(int8(eqim.*255)); %scalingrangebackto0->255androundingoffto

%integers

fori=0:255 %Findingtheequalizedhistogram

tmp=eqim-i;

n(i+1)=k-length(find(tmp));

end

figure,stem(1:256,n);

title('Equalizedhistogram'); %Plottingtheequalizedhistogram;

n=n/k;

sum(n)


11/12

%Remove'o'sfromstemplot

hs=gca;

h=get(hs,'children');

delete(findobj(h,'flat','Marker','o'))

%Tofindthecumulativedistribution(togetthenormalisedcurve)

s=0; %Variableinit

fori=1:256

s(i)=sum(n(1:i)); %s(i)=sumofelementsfromn(1)ton(i)(CDF)

end

figure,plot(s);

title('EqualizedCDF'); %Plotofthenormalizedcurve


12/12

AppendixAppendixAppendixAppendixII

%Programtofalsecolourofagrayscaleimageusingintensityslicingmethod

% Authors:

% VysakhPandSandeepNairC

% S5ECE

% AmritaSchoolofEngineering,Amritapuri;

%******************************************************************************* clear;

p=(imread('mri2.jpg')); %readinginthegrayscaleimagefigure,imshow(p); %displayingoriginalimage

p=double(p); %Typeconversionformathematicalmanipulation

[x,y]=size(p); %obtainingimagesize

fori=1:x

forj=1:y

pix=p(i,j);

ifpix

Doc _ Finale

Documents

Transcript of Doc _ Finale