Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 292
Improved Weighted Least Square Filter Based Pan Sharpening Using
Fuzzy Logic
Palwinder Kaur1, Er.Simranjit Kaur2
1 Department of Electronics &Communication Engineering ,
Sri Sai College of Engineering & Technology, Badhani, Pathankot, Punjab
2 Department of Electronics & Communication Engineering,
Sri Sai College of Engineering & Technology, Badhani, Pathankot, Punjab
----------------------------------------------------------------------------***-----------------------------------------------------------------------------
Abstract - Image fusion is growing to be one of the
most trendy plus interesting subject matter throughout
impression processing. In numerous software a number of
impression union techniques have been employed. An
important function to help blend impression will be
uniting crucial sides or maybe information connected with
various different images of only one particular field to
express simply just useful information. Methods
concerning individually distinct cosine alteration for
fusing images are generally appropriate and fewer time-
consuming throughout real-time systems.To take away the
downsides connected with the prior work a built-in
algorithm formula has become consist of on this paper.
The particular consist of algorithm formula combines the
improved label of PCA with fuzzy logic to help blend made
from images. The particular dimly lit route previous has
additionally been helpful to remove coloring artefacts plus
increase the shades on the end result image. This specific
innovative algorithm formula has become made plus
completed throughout MATLAB instrument using
impression control toolbox. The particular comparative
examination performed based on different operation
analyzing boundaries has demonstrated value of the
consist of algorithm.
Key Words: Image Fusion, Discrete Cosine
Transformation, Principle Component Analysis, Color
Artefacts, Multi-focus images.
1. INTRODUCTION
Image fusion is an active research area in digital image
processing. It is a mechanism of generating more
informative image from a set of source image. The main
objective of the image fusion is to combine the useful
information from several source of image in the same
image or picture or scene. At present image fusion has
become important image analysis and computer vision
technology, image fusion are broadly used in computer
vision, target recognition, remote sensing, satellite
imaging, robotics, medial image processing (intensity
modulated radiation therapy (IMRT)), military area, etc.
Simultaneously, image fusion has been successfully
applied to highly effective information for further image
processing, that is high efficiency video processing, image
detection and image recognition, image segmentation.
Figure 1.1(a): Input image focused on the foreground
(clock region)(b): Input image focused on the background
(books region) (c): Fused image
1.1 Levels of Image fusion
1. Pixel Level: This can be most straightforward process
within photograph combination carried out during
smallest level. In this put together the valuations and
intensities associated with a couple of feedback images
based upon it's regular, supplies the one resulting image.
2. Feature Level: The item justifies with all the highlights
of picture including if a person picture have their own
distorted observation different currently have distorted
just about any attribute including travel, nose. With this
higher level of approach conveniently get the actual
highlights of both equally related pictures separately and
then blend algorithm formula provides each boosted
picture just after attribute extraction.
3. Block or Region Based: In decision level fusion, it is
the highest level of the fusion. It extracts the information
of the data from pixel level or feature level fusion to create
optimal decision to reach a goal in specific objective.

Before the fusion, data should be obtain to gain the
absolute decision result, so that loss of information can’t
be ignore; meantime the cost is very high..
Fig.1 Various level of image fusion
1.2 WLS Filters
WLS filter is an edge-preserving filter, which usually
smoothes the whole picture when retaining the edges.
This has been put on numerous impression running
applications, such as developing variable quality
procedure plus firmness mapping. Compared to other
filtration systems, such as bilateral filter, the WLS filter
can certainly preserve the perimeters inside of a far better
fashion by looking into making the best skimp between
the blurring along with the sharpening. The particular
WLS filter as a low-pass filter for you to estimation the
LFCs of Pot plus MS image.
(‖ ‖ ( ( ) ( ) ))
Where the first term, i.e. ‖ ‖ ensures that the
distance between g and f is minimum. The second term is
to achieve the smoothness by minimizing the partial
derivatives of g. and are smoothness weights. λ is
the regularized factor to strike a balance between the two
terms.
Weighted least squares (WLS) Algorithm:
Input: An input image I, smoothing parameter λ, and
smoothness weights ax and ay.
Output: A smoothed base layer S.
Steps: 1) Ax ← diagonal matrix containing ax;
2) Ay ← diagonal matrix containing ay;
3) Dx ← discrete differentiation operator along the x
direction;
4) Dy ← discrete differentiation operator along the y
direction;
5) L ← DT x AxDx + DT y AyDy;
6) E ← identity matri
1.3 Pan Sharpening Algorithms
1. IHS Transformation Method
The IHS transform effectively transforms an image in the
Red-Green-Blue (RGB) domain into spatial (I) and spectral
(H, S) information [14]. There are various models of IHS
transformation available. The IHS transform effectively
transforms an image in the Red-Green-Blue (RGB) domain
into spatial (I) and spectral (H, S) information [14].
1.4 Fuzzy logic
Fuzzy logic idea is often as opposed to man being's
experience plus inference process. Compared with
conventional handle system, which often is indeed a point-
to-point handle, furred reasoning handle is often a range-
to-point or perhaps range-to-range handle [6]. This
production of any furred operator derives from
fuzzifications involving the two advices plus outputs with
the affiliated membership functions. A clean feedback is
likely to be turned into different folks the actual affiliated
membership capabilities predicated about its
value. Making use of this perspective, the actual
production of any furred reasoning operator is definitely
founded about its subscriptions of countless membership
capabilities, and this can be thought to be a variety of
inputs.To implement fuzzy logic technique to a real
application requires the following three steps:
1. Fuzzifications – convert classical details or fresh details
directly into fuzzy details or Membership rights
Performs (MFs) [5].
2. Fuzzy Inference Process – combine member's program
characteristics while using manage principles to gain a
fuzzy output [6].
3. Defuzzification - employ different processes to assess
each one connected production along
with organize them in to the table: the particular
research table. Grab the particular production from the
research kitchen table according to the existing
knowledge for the duration of software [5].
2. LITERATURE SURVEY
A.Soma Sekhar avec aussi al. (2011) [1] planned a whole
new multi-resolution algorithm criteria ideal for
unification by way of such as PCA in addition to wavelet
changes ideal for specialist diagnosis. Via combining the
characteristics of centre structured in addition to pixel-
based unification a whole new multi-resolution structured
unification is obviously attained. Amutha avec aussi al.
(2013) [2] encouraged an easy, fast and energy efficient
DCT structured multi-focus perception unification
software which in turn outperforms supplemental DCT

structured unification methods.The unification rule will
not require just about every difficult arithmetic hovering
place capabilities in particular advise or even variance
info, this isn't very difficult and energy efficient.Aribi, M
avec aussi al. (2012) [3] defined your evaluation around
the specialist perception top quality can be done through a
number of tactics of perception fusion. Information and
facts in order to normally always be highly processed from
the specialist graphics is obviously top-quality by way of
combining the knowledge by way of settled upon graphics
along with the unification technique's option is
determined by your application. During this papers your
MRI in addition to PET graphics tend to be taken ideal for
instance. Bedi S.S. avec aussi al. (2013) [4] displayed a
whole new reassesment on guides of perception
unification tactics in addition to perception top quality
evaluation parameters tend to be analysed to put together
your algorithm criteria ideal for perception unification in
which may appear far more acceptable ideal for health
care diagnosis.B.K. avec aussi al. (2013) [5] encouraged
that you be part of multifocus graphics from the
multiresolution DCT place instead of the wavelet place to
lower your computational complexity. The actual
evaluations on the complete functionality around the
merged perception from the encouraged place in addition
to that regarding your wavelet place in addition to 4
recently-proposed unification methods is obviously
done.The encouraged technique placed to several eyeglass
frames of multifocus graphics along with the operation as
soon as when compared successfully in addition to
quantitatively in addition to that regarding wavelets. Cao
avec aussi al. (2010) [6] gifted advice ideal for multi-focus
perception unification in addition to planned that must be
handling the artwork bin that is definitely accomplished
by way of graphics arrested by way of various goal points
having said that just about every thing mainly because
keeping arrested in addition to considered.Multi goal
boisterous perception unification algorithm criteria
making use of the personal needs let alter is becoming
proposed. Working together with kept info based upon
way via personal needs let alter,online property house
windows tend to be included in studying unification
weight. Desale, R.P avec aussi al. (2013) [7] offers found
your various methods that you be part of graphics such as
PCA, DCT in addition to DWT structured systems for
perception fusion.For better-quality in addition to
particular applications, your object rendering of DWT
structured unification process are planned within this
paper. Gintautas, Gary the gadget guy avec aussi al. (2011)
[8] offers encouraged any understanding unification
structure which in turn affords the adaptable photo image
resolution perception unification in addition to as well
help you save spectral benefits of graphics which have
been of reduce resolution. Pertaining to fusing adaptable
alarm truth in particular optical-optical, optical-radar
images a top place watch ideal for perception unification is
becoming proposed. Haghighat, H avec aussi al. (2010)
[9]introduced a fantastic process designed for multi-focus
perception unification made from calculations all-around
DCT domain. Almost all coefficients of DCT that happen to
be taken such as a requirement of difference all-around
perception handling applications computes the cost of
variance.. Haozheng, next s avec aussi al. (2011) [10]
displayed M-band Variable Towards previously mentioned
planned process, to begin with your adaptable goal
perception unification process based upon one wavelet as
well as adaptable wavelet, multi-band multi-wavelet is
regarded as together with arithmetic breaking down in
addition to reconstruction. During this papers, a variety of
tactics influenced by graphics, areas and specific zones in
addition to property house windows tend to be as soon as
when compared ideal for collection of unification
arithmetic operators.
3. METHODOLOGY
Fig 2: Flowchart of the proposed technique
4. RESULTS
For experimentation and implementation the proposed
technique is evaluated using MATLAB tool u2013a. The
Input Image
Apply fuzzy logic to evaluate membership
Evaluate low frequency and sub band coefficinets
Attain pan sharped image by fuzzy logic
Low frequency bands High frequency Band coefficients
Apply weighted least square filter for pan shapening
Evaluate parameters
End

evaluation of proposed technique is done on the origin of
following parameters i.e.maximum crosse correlation,
normalized absolute error and bit error rate based on
different images
Input image(a) Existing img(b) Proposed img(c)
Input image(a) Existing img(b) Proposed img(c)
Fig 3 Evaluation of different image fusion
As shown in above figure (a) is the input image and (b)
after using pan sharpening with WLS (c) is output image
after fuzzy logic with WLS i.e. proposed technique which
represent more enhanced results.
The following tables display cross-validation amongst
effective strategies along with the planned techniques.
Several operation assessment guidelines for digital
graphics are employed to establish the actual planned
algorithm's final results superior over existing algorithms.
1. Normalized Cross Correlation (NCC):
Normalized cross correlation is employed to evaluate
similar among the list of fused impression and also first
impression and it is depicted through these equation:
∑ ∑
Fig4: Maximum normalized cross correlation
2. Normalised Absolute Error (NAE):
The larger the value of normalized absolute error the
image quality degrades .NAE is mathematically
represented:
∑ ∑ | |
∑ ∑ ( )
Fig5 : Normalized absolute error
3. Bit Error Rate (BER):
Bit Error Rate provides each error cost for every single
pixel The smaller value of bit error charge the greater the
standard of image can be.
BER=1/PSNR

Fig6: Bit error rate
5. CONCLUSION
Image fusion incorporates details out of a lot of
illustrations of the same graphic to help reach the
beneficial image that's highly suitable for eyesight
producing applications.The image combination has grown
one of the main pre-processing methods of image
processing. Numerous image combination approaches are
created in many eyesight methods. A general aim for
undertaking combination is usually combining the handy
contents out of the variety of illustrations involving similar
graphic if you want to take precisely the handy material.
Individually distinct cosine remodel dependant strategies
are usually highly ideal for image combination and much
less difficult around true systems. DCT dependant
combination some time may perhaps result appropriate
outcomes because of combination method otherwise
known as combination artefacts. Consequently to be able
to get over this condition an integrated well-known fog
removals method “black sales channel before method” to
boost the effects additionally and take along
with artefacts may be proposed. On the other hand the
majority of the DCT structured approaches provides
focused entirely on grayscale illustrations or photos thus
integration involving PCA and also Fuzzy logic has been
specifically done to be able to authenticate the results for
color images.A comparing between active approaches just
like Fuzzy logic, DCT structured combination, PCA
structured combination,DWT structured combination and
also proposed method has been specifically done in
purchase to help examine the functional advancement
from the proposed formula to help authenticate the
proposed work. A comparison study executed according to
different efficiency assessing variables has revealed the
need for the proposed algorithm.
REFERENCES
Transform based fusion of multi-focus images for
visual sensor networks"Elsevier,2013.
[3] Aribi, Walid, Ali Khalfallah, Med Salami Bouhlel,
and NoomeneElkadri. “Evaluation of image fusion
techniques in nuclear medicine.” In Sciences of
Electronics, Technologies of Information and
Telecommunications (SETIT), 2012 6th
International Conference on, pp. 875-880. IEEE,
2012.
[4] Bedi S.S, AgarwalJyoti, AgarwalPankaj, “Image
fusion techniques and quality assessment
parameters for clinical diagnosis: A Review”,
International journal of advanced research in
computer and communication engineering Vol.
(2), issue 2, pp. 1153-1157, February 2013.
[5] B.K.Shreyamsha Kumar, M. N. S. Swamy, and M.
Omair Ahmad."Multiresolution DCT decomposition
for multifocus image fusion " In 26th IEEE
Canadian Conference Of Electrical And Computer
Engineering (CCECE),2013.
[6] Cao, Jian-zhong, Zuo-feng Zhou, Hao Wang, and
Wei-hua Liu."Multifocus Noisy Image Fusion
Algorithm Using the Contourlet Transform."In
Multimedia Technology (ICMT), 2010
International Conference on, pp. 1-4.IEEE, 2010.
[7] Desale, RajendaPandit, and Sarita V. Verma. “Study
and analysis of PCA, DCT & DWT based image
fusion techniques” In Signal Processing Image
Processing & Pattern Recognition (ICSIPR), 2013
International Conference on, pp. 66-69. IEEE,
2013.
[8] GintautasPalubinskas and Peter Reinartz. “Multi-
resolution, multi-sensor image fusion:general
fusion framework.” In Joint Urban Remote Sensing
Event, 2011 International Conference on, pp. 313-
316.IEEE, 2011.
[9] Haghighat, Mohammad BagherAkbari, Ali
Aghagolzadeh, and HadiSeyedarabi.“Real-time
fusion of multi-focus images for visual sensor
networks.”In Machine Vision and Image
Processing (MVIP), 2010 6th Iranian, pp. 1-6.IEEE,
2010.
[10] HaozhengRen, YihuaLan, and Yong Zhang. “
Research of Multi-Focus Image Fusion based on M-
band Multi-
Wavelet Transformation” In Fourth International
Workshop on Advanced Computational
Intelligence,2011 International Conference on, pp.
395-398. IEEE,2011.
[1] A. Soma Sekhar, Dr.M.N.Giri Prasad. “A Novel
Approach Of Image Fusion On MR And CT Images
Using Wavelet Transforms” IEEE Trans. on Image Proc.,
pp. 172-176. IEEE, 2011.
[2] Amutha, Y. AsnathVictyPhamila. "Discrete Cosine

[11] He, D-C., Li Wang, and MassalabiAmani.“A new
technique for multi-resolution image fusion.”In
Geoscience and Remote Sensing Symposium,
2004.IGARSS'04.Proceedings. 2004 IEEE
International, vol. 7, pp. 4901-4904. IEEE,
200KiranParmar, Rahul Kher. “A Comparative
Analysis of Multimodality Medical Image Fusion
Methods.” In Sixth Asia Modelling Symposium,
2012International Conference on, pp. 93-97. IEEE,
2012.
[12] Lavanya, A., K. Vani, S. Sanjeevi, and R. S. Kumar,
"Image fusion of the multi-sensor lunar image data
using wavelet combined transformation." In Recent
Trends in Information Technology (ICRTIT), 2011
International Conference on, pp.920-925. IEEE, 3-
5 June, 2011.
[13] Liang, Junping Du, JangMyung Lee, Qian Hu,
Zhenhong Zhang, Ming Fang, and Qian Wang.
"Multifocus image fusion using local perceived
sharpness." In Control and Decision Conference
(CCDC), 2013 25th Chinese, pp. 3223-3227. IEEE,
2013.
[14] Li, Hui, B. S. Manjunath, and Sanjit K.
Mitra.“Multisensor image fusion using the wavelet
transforms” Graphical models and image
processing, vol. 3, pp. 235-245.IEEE, 1997.
[15] Wang, Zhaobin, Yide Ma, and Jason Gu. "Multi-focus
image fusion using PCNN." Pattern Recognition 43.6
(2010): 2003-2016.
[16] Pan, Han, Gang Xiao, and Zhongliang Jing. "Feature-
based image fusion scheme for satellite
recognition." Information Fusion (FUSION), 2010
13th Conference on. IEEE, 2010.
[17] Xiao-Bo, Qu, et al. "Image fusion algorithm based on
spatial frequency-motivated pulse coupled neural
networks in nonsubsampled contourlet transform
domain." Acta Automatica Sinica 34.12 (2008):
1508-1514.
[18] Nencini, Filippo, et al. "Remote sensing image
fusion using the curvelet transform." Information
Fusion 8.2 (2007): 143-156.
[19] Liu, Zheng, et al. "Concealed weapon detection and
visualization in a synthesized image." Pattern
analysis and applications 8.4 (2006): 375.
[20] González-Audícana, María, et al. "Fusion of
multispectral and panchromatic images using
improved IHS and PCA mergers based on wavelet
decomposition." IEEE Transactions on Geoscience
and Remote sensing 42.6 (2004): 1291-1299.

Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic

Recommended

More Related Content

What's hot (18)

Similar to Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic (20)

More from IRJET Journal (20)

Recently uploaded (20)

Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic