Brief description on "Introduction to image contrast and enhancement method"

1. IMAGE CONTRAST
ENHANCEMENT METHODS
7-10-2015

2. ANALYSIS OF CONTRAST ENHANCEMENT
METHODS
 Contrast is the difference in
visual properties that makes
an object (or image)
distinguishable from other
objects and the
background.
 It is the different between
the darker and the lighter
pixel of the image, if it is big
the image will have high
contrast and in the other
case the image will have
low contrast.
CONTRAST DEFINITION

3. CONTRAST ENHANCEMENT METHODS
 The principal objective of enhancement is to process an
image so that the result is more suitable than the
original image for a specific application.
 For example, a method that is quite useful for
enhancing X-ray images may not necessarily be the
best approach for enhancing pictures of Mars
transmitted by a space probe.

4. Image
enhancement
Spatial
domain
methods
Frequency
domain
methods

5. SPATIAL DOMAIN METHODS
 The term spatial domain refers to the image plane
itself.
 Spatial domain methods are procedures that
operate directly on these pixels in an image.
 Spatial domain processes will be denoted by the
expression g(x,y)=T[f(x,y)], where f(x,y) is the input
image, g(x,y) is the processed image, and T is an
operator on f, defined over some neighborhood of
(x, y).

6. FREQUENCY DOMAIN METHODS
 Frequency domain processing techniques are
based on modifying the Fourier transform of an
image.
 More suitable for filtering spectrums.
 Any function that periodically repeats itself can be
expressed as the sum of sines and cosines of
different frequencies, each multiplied by a different
coefficient.

7. LOGARITHMIC TRANSFORMATION
 The general form is
s = c * log (1 + r),
where s is the output value, r is the input value and
c is a constant.
 This transformation maps a narrow range of low
gray-level values in the input image into a wider
range of output levels.
MATHEMATICAL MODELING

8. FLOW CHART FOR IMPLEMENTATION OF
LOGARITHMIC TRANSFORMATION

9. CODE FOR LOGARITHMIC TRANSFORMATION
im=imread('cameraman.tif');
subplot(231),imshow(im);
title('original image');
imd=im2double(im);
c=2.5;
d=0.5;
im3=c*log(1+imd);
im4=d*log(1+imd);
subplot(232),imshow(im3);
title('transformed image(c=2.5)');
subplot(233),imshow(im4);
title('transformed image(c=0.5)');
subplot(234),imhist(im);
title('histogram of the original image');
subplot(235),imhist(im3);
title('histogram of the transformed image(c=2.5)');
subplot(236),imhist(im4);
title('histogram of the transformed image(c=0.5)');

10. EXPERIMENTAL RESULTS

11. POWER-LAW TRANSFORMATION
 The general form is s = c * 𝐫 𝜸
,
where c and γ are positive
constants.
 Power-law curves with
fractional values of γ map a
narrow range of dark input
values into a wider range of
output values, with the
opposite being true for higher
values of input levels.

12. FLOW CHART FOR IMPLEMENTATION OF
POWER LAW TRANSFORMATION

13. CODE FOR POWER LAW TRANSFORMATION
im=imread('cameraman.tif');
subplot(231),imshow(im);
title('original image');
imd=im2double(im);
gamma=0.25;
im3=imd.^gamma;
gamma=2.5;
im4=imd.^gamma;
subplot(232),imshow(im3);
title('transformed image(gamma=0.25)');
subplot(233),imshow(im4);
title('transformed image(gamma=2.5)');
subplot(234),imhist(im);
title('histogram of the original image');
subplot(235),imhist(im3);
title('histogram of the transformed image(gamma=0.5)');
subplot(236),imhist(im4);
title('histogram of the transformed image(gamma=2.5)');

14. EXPERIMENTAL RESULTS

15. GAMMA CORRECTION
 The exponent in the
power-law equation is
referred to as gamma.
The process used to
correct this power-law
response phenomena
is called gamma
correction.
 The process used to
correct power-law
response phenomena
is called gamma
correction.
 𝟏. 𝟖 < 𝛄 < 2.5

16. HISTOGRAM EQUALIZATION
 The general form is
sk=
L−1 ∗(rk−rkmin)
rkmax−rkmin
where
k=0,1,2,…L-1, r and s are
the input and output pixels of
the image, L is the different
values that can be the pixels,
and rkmax and rkmin are the
maximum and minimum gray
values of the input image.
 This method usually increase
the global contrast of the
image. This allows for area’s
of lower contrast to gain
higher contrast.

17. EXPERIMENT RESULTS

18. CODE FOR HISTOGRAM EQUALIZATION
input_image=imread('dollars.tif');
input_image_process=input_image;
output_image_process=histeq(input_image_process);
output_image=im2uint8(mat2gray(output_image_process));
input_hist=imhist(input_image);
output_hist=imhist(output_image);
subplot(2,2,1),imshow(input_image),title('Input image')
subplot(2,2,2),imshow(output_image),title('Output image')
subplot(2,2,3),plot(input_hist),title('Input histogram')
xlabel('Gray levels')
ylabel('Relative frecuency')
set(gca, 'xlim', [0 255]);
subplot(2,2,4),plot(output_hist),title('Output histogram')
xlabel('Gray levels'),ylabel('Relative frecuency')
set(gca, 'xlim', [0 255]);

19. FLOW CHART FOR HISTOGRAM EQUALIZATION

20. ADVANTAGES
 The method is useful in images with backgrounds and
foregrounds that are both bright or both dark.
 A advantage of the method is that it is a fairly
straightforward technique and an invertible operator.
DISADVANTAGE
 A disadvantage of the method is that it is indiscriminate.
It may increase the contrast of background noise, while
decreasing the usable signal.

21. CONTRAST STRETCHING
 Low-contrast images can result from poor
illumination, lack of dynamic range in the imaging
sensor, or even wrong setting of a lens aperture
during image acquisition.
 The idea behind contrast stretching is to increase
the dynamic range of the gray levels in the image
being processed.

22. CONTRAST STRETCHING FLOWCHART

23. CODE FOR CONTRAST STRECHING
im=imread('cameraman.tif');
subplot(231),imshow(im);
title('original image');
imd=double(im);
m=80;
e=3;
im3=1 ./ (1 + (m./imd).^e);
subplot(222),imshow(im3);
title('contrast stretched image(m=80,e=3)');
subplot(223),imhist(im);
title('histogram of the original image');
subplot(224),imhist(im3);
title('histogram of contrast stretched image(m=80,e=3)');

24. EXPERIMENTAL RESULTS

25. APPLICATION
 (Left) Original sensed fingerprint; (center) image
enhanced by detection and thinning of ridges;
(right) identification of special features called
minutia", which can be used for matching to millions
of fingerprint representations in a database.

26. CONCLUSION
 Image enhancement is basically improving the
interpretability or perception of information in images for
human viewers and providing `better' input for other
automated image processing techniques.
 For dark images with low contrast the better results will be
with the logarithm and the power law transformations using
in the second one gamma values lower than 1.
 For light images it would be use the power law
transformation with gamma higher than 1.
 For image with low contrast in gray scale the better methods
are histogram equalization and contrast stretching.

27. THANK YOU