This document provides a comparative study of various image fusion methods in the spatial domain. It begins by introducing image fusion and its applications. Section 2 then describes several common fusion algorithms in the spatial domain, including average, select maximum/minimum, Brovey transform, intensity hue saturation (IHS), and principal component analysis (PCA). Section 3 defines image fusion quality measures like entropy, mean squared error, and normalized cross correlation. Section 4 provides a comparative analysis of the spatial domain fusion techniques based on parameters like simplicity, type of resources, and disadvantages. It finds that spatial domain methods provide high spatial resolution but have issues like image blurring and producing less informative outputs. The document concludes that while the best algorithm depends on the problem, spatial

1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME
161
COMPARATIVE STUDY ON IMAGE FUSION METHODS IN SPATIAL
DOMAIN
Prof. Keyur N. Brahmbhatt1
Assistant Professor, I.T Department
B.V.M Engineering College
VallabhVidyaNagar-388120, Gujarat, India.
Dr. Ramji M. Makwana2
Associate Professor, Computer Department
ADIT Engineering College
VallabhVidyaNagar-388120, Gujarat, India.
ABSTRACT
Image fusion is a process of combining images, obtained by sensors of different
wavelengths simultaneously viewing of the same scene, to form a composite image. The
composite image is formed to improve image content and to make it easier for the user to
detect, recognize, and identify targets and increase his situational awareness. The research
activities are mainly in the area of developing fusion algorithms that improves the
information content of the composite imagery, and for making the system robust to the
variations in the scene, such as dust or smoke, and environmental conditions, i.e. day or and
night. This paper is structured in the following way: section 1 gives introduction to image
fusion. Section 2 provides details on several fusion algorithms. Section 3 defines a set of
image fusion measures of effectiveness. Section 4 provides a comparative study of the fusion
techniques in spatial domain finally; Section 5 provides a summary of the paper and its main
conclusions.
Keywords: Spatial domain, Select Maximum/minimum, PCA, HIS, Bovey Transform
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN
ENGINEERING AND TECHNOLOGY (IJARET)
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 2 March – April 2013, pp. 161-166
© IAEME: www.iaeme.com/ijaret.asp
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2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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I. INTRODUCTION
Image fusion means the combining of two images into a single image that has the
maximum information content without producing details that are non-existent in the given
images. [2] With rapid advancements in technology, it is now possible to obtain information
from multi source images to produce a high quality fused image with spatial and spectral
information. Image Fusion is a mechanism to improve the quality of information from a set of
images. Important applications of the fusion of images include medical imaging, microscopic
imaging, remote sensing, computer vision, and robotics.[7] Recently, Discrete Wavelet
Transform (DWT) and Principal Component Analysis (PCA), Morphological processing and
Combination of DWT with PCA and Morphological techniques have been popular fusion of
image. These methods are shown to perform much better than simple averaging, maximum,
minimum. [1]
II. IMAGE FUSION ALGORITHM
A. Average Method
Here, the resultant image is obtained by averaging every corresponding pixel in the
input images [4]
• Advantage
1) It is very simple method.
2) Easy to understand and implement.
3) Averaging works well when images to be fused from same type of sensor and contain
additive noise.
4) This method proves good for certain particular cases where in the input images have an
overall high brightness and high contrast.
• Disadvantages
1) It leads to undesirable side effect such as reduced contrast.
2) With this method some noise is easily introduced into the fused image, which will reduce
the resultant image quality consequently. [3]
B. Select Maximum/Minimum Method
A selection process if performed here wherein, for every corresponding pixel in the
input images, the pixel with maximum/minimum intensity is selected, respectively, and is put
in as the resultant pixel of the fused image. [4]
• Advantage
1) Resulting in highly focused image output obtained from the input image as compared to
average method [6]
• Disadvantage
1) Pixel level method are affected by blurring effect which directly affect on the contrast of
the image [6]

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C. Brovey Transform
Brovey transform (BT) , also known as color normalized fusion, is based on the
chromaticity transform and the concept of intensity modulation .It is a simple method to
merge data from different sensors, which can preserve the relative spectral contributions of
each pixel but replace its overall brightness with the high spatial resolution image .As applied
to three MS bands, each of the three spectral components (as RGB components) is multiplied
by the ratio of a high-resolution co-registered image to the intensity component I of the MS
data [3]
• Advantages
1) It is a simple method to merge the data from different sensors.
2) This method is simple and fast.
3) It provide superior visual and high resolution multispectral image.
4) Very useful for visual Interpretation.
• Disadvantages
1) This method ignores the requirement of high quality synthesis of spectral information.
2) It produces spectral distortion. [3]
D. Intensity Hue Saturation (IHS)
It is most popular fusion methods used in remote sensing. The fusion is based on the
RGB-IHS conversion model, whose various mathematical representations have been
developed .No matter which conversion model is chosen, the principle of the IHS
transformation to merge images attributes to the fact that the IHS color space is catered to
cognitive system of human beings and that the transformation owns the ability to separate the
spectral information of an RGB composition in its two components H and S, while isolating
most of the spatial information in the I component. In this method three MS bands R, G and
B of low resolution Image are first transformed into the IHS color coordinates, and then the
histogram - matched high spatial resolution image substitutes the intensity image which
describes the total color brightness and exhibits as the dominant component a strong
similarity to the image with higher spatial resolution. Finally, an inverse transformation from
IHS space back to the original RGB space yields the fused RGB image, with spatial details of
the high resolution image incorporated into it .The intensity I defines the total color
brightness and exhibits as the dominant component . After resolution using the high
resolution data, the merge result is converted back into the RGB After applying HIS. [3]
• Advantages
1) It provides high spatial quality.
2) It is a simple method to merge the images attributes.
3) It provides a better visual effect.
4) It gives the best result for fusion oh remote sensing images.
• Disadvantages
1) It produces a significant color distortion with respect to the original image.
2) It suffers from artifacts and noise which tends to higher contrast.
3) The major limitation that only three bands are involved [3]

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E. Principal Component Analysis Algorithm
Principal component analysis (PCA) is a vector space transform often used to reduce
multidimensional data sets to lower dimensions for analysis. It reveals the internal structure
of data in an unbiased way.
• Advantages
1) This method is very simple to use and the images fused by this method have high spatial
quality.
2) It prevents certain features from dominating the image because of their large digital
numbers.
• Disadvantages
1) It suffers from spectral degradation.
2) This method is highly criticized because of the distortion of the spectral Characteristic
between the fused images and the original low resolution Images. [3]
III. MEASURING TECHNIQUE
A. ENTROPY (EN)
Entropy is an index to evaluate the information quantity contained in an image. If the
value of entropy becomes higher after fusing, it indicates that the information increases and
the fusion performances are improved. Entropy is defined as:-
L-1
E = - ∑ pi log 2 pi
i=0
Where L is the total of grey levels, p= {p0, p1…pL-1} is the probability distribution of each
level. [1]
B.MEAN SQUARED ERROR (MSE)
The mathematical equation of MSE is giver by the equation
m n
MSE = 1 ∑ ∑ (Aij-Bij)2
mn i=1 j=1
Where, A - the perfect image, B - the fused image to be assessed, i – pixel row index, j – pixel
column index, m, n- No. of row and column [1][5]
C. NORMALIZED CROSS CORRELATION (NCC)
Normalized cross correlation are used to find out similarities between fused image and
registered image is given by the following equation [1]

5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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m n
∑ ∑ (Aij * Bij)
NCC = i=1 j=1
m n
∑ ∑ (Aij)2
i=1 j=1
IV. COMPARATIVE STUDY OF SPATIAL IMAGE FUSION TECHNIQUE
Here we have made comparison of various image fusion methods in spatial domain.
Measuring
Parameter
Average
Method
Maxima
/Minima
method
Brovey Method IHS PCA
Simplicity Simple and easy
to implement
Simple method simple and fast
method
Simple
method
Simple
method
Type of
resources
Fused image
from same type
of sensor
Fused image
from same
type of sensor
Merge the data
from
Different
sensors.
Merge the
data from
Different
sensors.
Disadvantage Reduced
contrast.
Create blurring
effects
spectral
distortion
color
distortion
spectral
degradation
Disadvantage If some noise is
introduced , it
will reduce the
resultant image
quality
consequently
It has higher
pixel intensity
but it does not
means always
give better
information.
This method
ignores the
requirement of
high quality
synthesis of
spectral
information.
It suffers
from
artifacts and
noise which
tends to
higher
contrast.
Resulting
image
does not
preserve
faithfully the
colors found
in the original
images
V. CONCLUSION
Although selection of fusion algorithm is problem dependent but this review results
that spatial domain provide high spatial resolution and easy to perform, but spatial domain
have image blurring problem and their outputs are less informative.
VI. REFERENCES
[1] Deepak Kumar Sahu, M.P.Parsai, “Different Image Fusion Techniques –A Critical
Review”, IJMER, Vol. 2, Issue. 5, Sep.-Oct. 2012 pp-4298-4301 ISSN: 2249-6645
[2] Firooz Sadjadi, “Comparative Image Fusion Analysis”
[3] Nupur Singh , Pinky Tanwar, “Image Fusion Using Improved Contourlet Transform
Technique” IJRTE ISSN: 2277-3878, Volume-1, Issue-2, June 2012

6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 2, March – April (2013), © IAEME
166
[4]Shivsubhamani,K.P.Soman “Implementation and Comparative Study of Image Fusion
Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 9–
No.2, November 2010
[5] Shivsubramani Krishnamoorthy, Development of Image Fusion Techniques And
Measurement Methods to Assess the Quality of the Fusion
[6] Vidhya K P , Saritha E S, “A Comparative Study on Medical Image Fusion Technique.
[7] Xydeas, C., and Petrovic, V., “Objective Pixel-level Image Fusion Performance
Measure,” Sensor Fusion: Architectures, Algorithms, and Applications IV, SPIE Vol. 4051,
pp. 89-98, 2000.
[8] Benayad Nsiri, Salma Nagid and Najlae Idrissi, “New Approach to Multispectral Image
Fusion Based on a Weighted Merge” International Journal of Electronics and Communication
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0976- 6464, ISSN Online: 0976 –6472.
[9] Dr. Sudeep D. Thepade and Jyoti S.Kulkarni, “Novel Image Fusion Techniques using
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– 6367, ISSN Online: 0976 – 6375.
[10] I.Suneetha and Dr.T.Venkateswarlu, “Spatial Domain Image Enhancement using
Parameterized Hybrid Model”, International Journal of Electronics and Communication
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