18-01-2013, 03:35 PM
A Comprehensive Review of Image Enhancement
Techniques
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Introduction
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. The principal objective of image
enhancement is to modify attributes of an image to make it more
suitable for a given task and a specific observer. During this
process, one or more attributes of the image are modified. The
choice of attributes and the way they are modified are specific to
a given task. Moreover, observer-specific factors, such as the
human visual system and the observer's experience, will introduce
a great deal of subjectivity into the choice of image enhancement
methods. There exist many techniques that can enhance a digital
image without spoiling it. The enhancement methods can broadly
be divided in to the following two categories:
1. Spatial Domain Methods
2. Frequency Domain Methods
In spatial domain techniques [1], we directly deal with
the image pixels. The pixel values are manipulated to achieve
desired enhancement. In frequency domain methods, the image is
first transferred in to frequency domain. It means that, the Fourier
Transform of the image is computed first. All the enhancement
operations are performed on the Fourier transform of the image
and then the Inverse Fourier transform is performed to get the
resultant image. These enhancement operations are performed in
order to modify the image brightness, contrast or the distribution
of the grey levels. As a consequence the pixel value (intensities)
of the output image will be modified according to the
transformation function applied on the input values.
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Raman Maini is working as a Reader (Computer Engineering),
University College of Engineering, Punjabi University, Patiala.
Himanshu Aggarwal is working as a Reader (Computer
Engineering), University College of Engineering, Punjabi University,
Patiala.
Image enhancement is applied in every field where
images are ought to be understood and analyzed. For example,
medical image analysis, analysis of images from satellites etc.
Image enhancement simply means, transforming an
image f into image g using T. (Where T is the transformation. The
values of pixels in images f and g are denoted by r and s,
respectively. As said, the pixel values r and s are related by the
expression,
s = T® (1)
Where T is a transformation that maps a pixel value r into a pixel
value s. The results of this transformation are mapped into the
grey scale range as we are dealing here only with grey scale
digital images. So, the results are mapped back into the range [0,
L-1], where L=2k, k being the number of bits in the image being
considered. So, for instance, for an 8-bit image the range of pixel
values will be [0, 255].
I will consider only gray level images. The same theory can be
extended for the color images too. A digital gray image can have
pixel values in the range of 0 to 255.
Figure 1. Showing the effect of Image Enhancement
Many different, often elementary and heuristic methods
[2] are used to improve images in some sense. The problem is, of
course, not well defined, as there is no objective measure for
image quality. Here, we discuss a few recipes that have shown to
be useful both for the human observer and/or for machine
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH 2010, ISSN
recognition. These methods are very problem-oriented: a method
that works fine in one case may be completely inadequate for
another problem. In this paper basic image enhancement
techniques have been discussed with their mathmatical
understanding. This paper will provide an overview of underlying
concepts, along with algorithms commonly used for image
enhancement. The paper focuses on spatial domain techniques for
image enhancement, with particular reference to point processing
methods, histogram processing.
Point Processing Operation
The simplest spatial domain operations occur when the
neighbourhood is simply the pixel itself. In this case T is referred
to as a grey level transformation function or a point processing
operation. Point processing operations take the form shown in
equation (1)
Figure 2. Figure shows basic grey level
transformations
Create Negative of an Image
The most basic and simple operation in digital image processing
is to compute the negative of an image. The pixel gray values are
inverted to compute the negative of an image. For example, if an
image of size R x C, where R represents number of rows and C
represents number of columns, is represented by I(r, c). The
negative N(r, c) of image I(r, c) can be computed as
N(r, c) = 255 – I(r, c) where 0 <= r <= R and 0 <= c <= C
(2)
It can be seen that every pixel value from the original image is
subtracted from the 255. The resultant image becomes negative of
the original image. Negative images [3] are useful for enhancing
white or grey detail embedded in dark regions of an image.
s = 1.0 - r
Figure 3 Note how much clearer the tissue is in the negative
image of the mammogram
s = intensitymax - r (3)
Thresholding Transformations
Thresholding transformations [4] are particularly useful for
segmentation in which we want to isolate an object of interest
from a background as shown in figure below
Figure 4. Showing effect of thresholding transformation
for isolating object of interest
2.3 Intensity Transformation
Original Image x
y Image f (x, y)
Enhanced x
y Image f (x,
s =
1.
0. r <=
r >
Original Image x
y Image f (x, y)
Enhanced x
Y Image f (x,
s =
0.0 r <= threshold
1.0 r > threshold
JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH 2010, ISSN 2151-9617
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2.4 Logarithmic Transformations
The general form of the log transformation is
s = c * log (1 + r)
(4)
The log transformation maps [5] a narrow range of low input grey
level values into a wider range of output values. The inverse log
transformation performs the opposite transformation. Log
functions are particularly useful when the input grey level values
may have an extremely large range of values. In the following
example the Fourier transform of an image is put through a log
transform to reveal more detail
s = log(1 + r)
Figure 5. Example showing effect of Logarithmic transformation
s = log(1 + r) (5)
We usually set c to 1. Grey levels must be in the range
[0.0, 1.0]
2.5 Powers-Law Transformations
The nth power and nth root curves shown in fig. A can
be given by the expression,
s = crγ (6)
This transformation function is also called as gamma
correction [6]. For various values of γ different levels of
enhancements can be obtained. This technique is quite commonly
called as Gamma Correction. If you notice, different display
monitors display images at different intensities and clarity. That
means, every monitor has built-in gamma correction in it with
certain gamma ranges and so a good monitor automatically
corrects all the images displayed on it for the best contrast to give
user the best experience. The difference between the logtransformation
function and the power-law functions is that using
the power-law function a family of possible transformation curves
can be obtained just by varying the λ. These are the three basic
image enhancement functions for grey scale images that can be
applied easily for any type of image for better contrast and
highlighting. Using the image negation formula given above, it is
not necessary for the results to be mapped into the grey scale
range [0, L-1]. Output of L-1-r automatically falls in the range of
[0, L-1]. But for the Log and Power-Law transformations
resulting values are often quite distinctive, depending upon
control parameters like λ and logarithmic scales. So the results of
these values should be mapped back to the grey scale range to get
a meaningful output image. For example, Log function s = c log
(1 + r) results in 0 and 2.41 for r varying between 0 and 255,
keeping c=1. So, the range [0, 2.41] should be mapped to [0, L-1]
for getting a meaningful image.