Journal of Signal and Information Processing, 2013, 4, 359-363
Published Online November 2013 (http://www.scirp.org/journal/jsip)
http://dx.doi.org/10.4236/jsip.2013.44045
Open Access JSIP
359
Adaptive Enhancement Techniques for Solar Images
Mohammad A. A. Al-Rababah1, Abdusamad Al-Marghilani1, Mohammed M. Al-Shomrani2,
Ibrahim A. Atoum3
1Northern Border University, Arar, KSA; 2King Abdulaziz University, Jeddah, KSA; 3Hail University, Hail, KSA.
Received July 23rd, 2013; revised August 23rd, 2013; accepted August 31st, 2013
Copyright © 2013 Mohammad A. A. Al-Rababah et al. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
ABSTRACT
Radio astronomy radio telescope plays the role of a linear operator, affecting the function that describes the object of
research, formation of image of a monitored object. This paper presents methods for reconstruction and correction of
solar radio images using the algorithm of rejections, the updated Weiner-filter, and the method CLEAN designed by
Hëgbomom (Pseudonym, 2009) for point sources. It is the process of numerical convolution in signal handling, an al-
gorithm for separating weak-contrast formations on the solar which represents most points of the actual limb by using
the ellipse equation. Consequently, the filling algorithm is applied by moving from the center to the ellipse points and
filling each point by solar image data. Finally, a linear limb-darkening expression is used to remove the limb darkening.
Different examples of the intermediate and final results are presented in addition to the developed algorithm.
Keywords: Image Processing; Solar Imaging; Image Enhancement; Linear Transformation Functions; Limb Darkening;
Solar Disk; Limb Fitting
1. Introduction
At the present day the problem of object detection on
digital picture is very challenging due to rapid develop-
ment of photo and video electronics. Despite the fact that
physical reality contains a lot of different objects the de-
velopment of detection algorithm for a narrower class of
objects—human faces—is of considerable interest [1].
This is due to the increasing degree of automation of
various processes and production systems. The particular
application of the algorithm of human faces detection
may be as follows: automatic registration of visitor’s
number in the supermarkets and entertainment [2].
Centres crossing control systems in various institutions,
airports, subway; automated systems to prevent accidents,
they monitor the face of vehicle’s driver; man-machine
intelligent interfaces, current demands in establishment
of such systems impose strict limits on the speed of the
algorithms executions, which shall operate in real time
mode. Hence, the perspective problem to be solved is
creation of fast and reliable algorithm of human faces
detection.
Available approaches are to face the detection prob-
lem. In the last ten years, the dynamic elaborations are
conducted in the field of images detection and there were
offered a variety of detection methods: the method of
principal components, methods with using of histograms,
neural network, Bayesian networks, statistical methods, etc.
Some of these detecting algorithms are invariant ones
with respect to the object, while others use such a priori
knowledge about the object, like the shape, colours, rela-
tive position of parts [3].
In computer graphics, image enhancement is one class
of five different classes of digital image processing op-
erations. These classes represent the fundamental tech-
niques of this field. An image enhancement operation
seeks to improve the quality of a digitally stored image
by manipulating the image with software. This opera-
tion may be an end itself or could be used as a preproc-
essing phase to ease the next-processing step or as post-
processing steps to improve the visual perception of a
processed image. Some images need an easy enhance-
ment operation while other images need more sophis-
ticated operations. One of these later images is solar
images. These images have to be pre-processed in or-
der to correct them for geometrical or photometric dis-
tortions.
One of the initial efforts in this area is the standardiza-
tion techniques.

Adaptive Enhancement Techniques for Solar Images 360
2. Developed by Solar Images
One of the most successful recovery procedures is clean-
ing algorithm (CLEAN), developed for point sources. It
is the process of numerical convolution in signal han-
dling. Sizes, requiring many thousands of method of
cleansing, to stabilize and reduce the distorting effects:
cleaning, stabilized anti-aliasing and cleaning method of
cleaning, stabilized intrude. Low value enhancing algo-
rithm downward distortion is efficient for cleaning, stabi-
lized anti-aliasing at the asymptotic behavior of the pro-
cess. In addition, it is worth noting that the second
methodology cleaning, stabilized intrude cannot be prac-
tically used, because the process does not always con-
verge [4].
For feature selection for each iteration of the loop
method is used. Valuable qualities are especially notice-
able when processing algorithm of extended areas, a brief
look at a classical method. Look at the Equation (1) in
the one-dimensional version:
  
  
1
d
,
ii
k
k
i
g
xhxxfxx
fxpkx x 





 (1)
where is a function of the coefficients the Equa-
tion (2)

pk
 



 
  

 
11
1
11
11
11
11
1
ii
i
ii
ik
N
ii
ki
k
k
gxhxxf x
hx xxpkxx
pkhx xxx
pkhx x










 






k
k

(2)
From here you can see that the function


g
x is
visualized as a sum of an infinite number of functions-
oriented diagrams. The function

g
x is called “Dirty”
card, a function


hx-“dirty beam”. From these consid-
erations it should be purely empirical algorithm associ-
ated with the subtract join dirty beam from a dirty cards
distribution granularity the brightness on the replies from
point sources, and then replacing each of them for the
response of the “pure”.
1) The most vivid picture element (a) in a distorted
image, such that the element of l, m j, k a l all > ≠ j and
m ≠ k.
2) A new refined image is PD-l, the Equation (3)

1, , ,,
,, ,
,
1.
mjkijmkim
imjkijm kim
him
fc afc
aa ha
,












(3)
Refined image is collapsed with the perfect beam of


hx, with the main lobe and strongly reduced side, the
main advantage of the method is its simplicity. Negative.
The quality of the algorithm is a small calculation
speed and apparent distortions in the form of wrinkles
and “flings” structure, which manifests itself in the ex-
tended areas. It is worth mentioning that the algorithm
does not contain clear criteria for selecting options in-
crease [5].
3. Material and Methods
Geometrical model of thin layer matting model to pro-
duce a quantitatively collation of the darkening of the
Sun to the edge of the visible wavelength range is a thin
layer of emitting model. According to this model, the
entire visible spectrum of the Sun is emitted at a certain
depth VIS
H
Therefore, Figure 1 model to produce a
quantitative calculation.
By known dependencies


,Tl


B
T radial tem-
perature distribution and function respectively source
can build the intensity of radiation output the Equation
(4).

 
22
112 1
,.
VIS VIS
VIS
BTH Hr
IrHBT H


 




 (4)
Model of emitting layer final thickness.
In this case the value normalized to 1 the intensity is
determined by the integration of line of sight and an ex-
pression
s
s


of the beam the Equation (5)
 


 
  
0
0
,,
,
d
H
H
BT hlrhlrl
IrH
BT lll




d
(5)
,,BT

Figure 1. Radial slices of sun-illustration to describe geo-
metric models of the atmosphere.
Open Access JSIP

Adaptive Enhancement Techniques for Solar Images 361

2
,11 21hlrl lr 2 (6)
In the assumption of equilibrium nature of the proc-
esses


4
~BTT involved. The possibility of such a
deal may be justified, by calculating the Equation (7).
00
d
JBChh
 

d







(7)
The ratio of the radiant flux to the average number of a
volume. For the Sun compared the value negligibly
small.
C
Grey model of the atmosphere

  does not de-
pend on the equation we obtain the integral equation of
linear on the singular kernel the Equation (8).
(
 
Bcq

 



) (8)
 

 
1
2
02
exp d
8π
0,1 Arth2
F
qq
I





 









, (9)

π2
23
0
61 31d
π1ctg π
q

 







 (10)
 
0
2
π2
222
0
d3
0, e1
4
sin
ln 1ctg
exp d
πcos sin
tt
IBt F






 


















(11)
4. Erosion and Dilation
Almost all types of solar activity very effective evident in
the microwave the radiation of the Sun. Monitoring of
solar active-Ness, including the faint-events systemati-
cally runs from sunrise to sunset The Sun against the
background of the solar disk in microwave-PTO radiation
with high spatial and temporal resolution, the weakly
contrasting formations contain valuable information needed
to examine the conditions of emergence, peculiarities of
evolution and prediction of solar-terrestrial interaction.
When identifying physical properties of Coronal holes,
prominences and filaments important identification of
these entities on a solar disk. In the microwave radiation
it is difficult. Proposed earlier identification tools require
observation of the Sun with high spatial resolution on
two or more wavelengths, which is not always possible.
The Sun takes place in several stages. At the first pre-
liminary processing phase noise filtering produced: 1)
Filters, remove obstacles; That linked to the inaccuracy of
the calibration coefficient gain and zero level channels; 2)
Median filter for eliminating pulse noise; 3) Low fre-
quency filter for removing noise outside the bandwidth to
In general, a recursive filter was used. In the task of
identification of fibers, which are typically an elongated
East-West area, type of filter applied, obviously, the
bigger the SE size is, the longer the time spent by the
computer calculating all the values will be. This part of
the detection is the most time consuming in the computa-
tional process. Figure 2 summarizes the whole process
[6,7].
Initially, the exponential function is expressed and
computed for every intensity value that is less than an
empirical value



, otherwise express and calculate
the logarithmic value for the underlying intensity value.
Suppose we have any two positive real numbers a and b
then calculates the value of m as in Equation (12) [8,9].

log 1ma mb

(12)
where is the maximum intensity value in the under-
lying image (255). Then calculate b as in Equation (13):
m
1expm
a
bm



 (13)
Given a constant α, pixels in original image O (i, j) are
Figure 2. Chart diagram of the sunspot detection proce-
dure.
Open Access JSIP

Adaptive Enhancement Techniques for Solar Images 362
modified as in Equation (3) to obtain the required en-
hanced image E (i, j):
If

,Oij

, then
 
,log1 ,Eij abOij



 (14)
else
 
,
,exp 1
oij
Eij b
a









(15)
The value of beta (β) was empirically found to be 10,
whereas the alpha (α) value is 100 and a =1000. The al-
gorithm has the effect to remove the background and
reside the foreground [9,10] the result of this is shown in
Figure 3.
5. Discussion and Result
Where the semi-major axis and b is is the semi-minor
axis, as illustrated in Figure 4.
(a) (b)
Figure 3. Results of detecting the solar disk. (a) The original
solar image observed at Meudon Observatory on 31/7/ 2001;
(b) The solar disk detected.
(a) (b)
(c)
Figure 4. (a) Semi major and minor axis; (b) The actual
limb and the ellipse drawn; (c) The ellipse after removing
the actual limb point.
6. Filling the Ellipse
The filling step was designed to make a distinction be-
tween the solar object region and the background region
that to be positioned outside the solar object. It is applied
by moving from the candidate center points toward the
ellipse points and filling every point inside the ellipse
and not related to the limb by the actual solar image pix-
els. Figure 5 shows the ellipse after filling it with the
solar disk data [10,11].
Limb Darkening Removal
The sun is not equally bright all over, but it is darkened
towards the limb, it is a phenomenon called limb dark-
ening which should be calculated and removed. A sensi-
ble empirical representation of the limb darkening is
governed by the linear limb-darkening expression shown
in Equation (5) [12].


 
11
1
Iu
I





(16)
I (1) is the specific intensity at the center of the disk,
u is the LDC (Limb-arkening coefficient), it's empirically
found to be equal 0.5, and


cos

 , where θ is the
angle between the line of sight and the emergent intensity.
Figure 6(a) shows an original solar image and Figure
6(b) shows it after limb-darkening removed. These en-
hanced images could be used then for the post-processing
applications like detecting and tracking [13,14].
Solar features as shown in Figure 7. Figure 7(a) shows
an enhanced image and Figure 7(b) shows the image
after applying an adaptive thresholding technique for
detecting solar filament.
7. Conclusions
This presented work was to detect a cleaned solar disk as
precisely as possible that could be used for different
techniques of solar image analysis. Different tools were
developed in this paper to achieve this goal initially by
detecting the solar disk and removing the background
spots by using linear exponential and logarithmic func-
Figure 5. Filled solar disk for solar image observed at
Meudon Observatory on 31/7/2001.
Open Access JSIP

Adaptive Enhancement Techniques for Solar Images
Open Access JSIP
363
(a) (b)
Figure 6. (a) Original image observed at Meudon Observa-
tory on 1/04/2002; (b) The result removing limb darkening.
(a) (b)
Figure 7. (a) An original Image observed at Meudon Ob-
servatory on 31/7/2001; (b) The result after applying ALT.
tions. Then, we determine the solar limb by using mor-
phological operations.
This gives the chance to determine the initial estima-
tion of the solar disk radius and center. Thereafter, using
an elliptical equation, the elliptical shape of the solar disk
is drawn which approximately includes most of the initial
estimations of the solar limb. This process is followed by
filling the elliptical shape with the actual solar disk data,
and finally removing the limb darkening. Regardless of
this progress, there are still some challenges that are not
solved.
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