Design and implementation a prototype system for fusion image by using SWT-PCA algorithm with FPGA technique

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 1, February 2020, pp. 757~766
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i1.pp757-766  757
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Design and implementation a prototype system for fusion image
by using SWT-PCA algorithm with FPGA technique
Muthna Jasim Fadhil1
, Rashid Ali Fayadh2
, Mousa K. Wali3
1
Department of Electrical Power Engineering, Electrical Engineering Technical College,
Middle Technical University (MTU), Iraq
2,3
Department of Medical Instrumentation Engineering Techniques, Electrical Engineering Technical College,
Middle Technical University (MTU), Iraq
Article Info ABSTRACT
Article history:
Received Jan 16, 2019
Revised Sep 17, 2019
Accepted Sep 27, 2019
The technology of fusion image is dominance strongly over domain research
for recent years, the techniques of fusion have various applications in real
time used and proposed such as purpose of military and remote sensing etc.,
the fusion image is very efficient in processing of digital image. Single image
produced from two images or more information of relevant combining
process results from multi sensor fusion image. FPGA is the best
implementation types of most technology enabling wide spread.This device
works with modern versions for different critical characteristics same huge
number of elements logic in order to permit complex algorithm implemented.
In this paper,filters are designed and implemented in FPGA utilized for
disease specified detection from images CT/MRI scanned where the samples
are taken for human's brain with various medical images and the processing
of fusion employed by using technique Stationary Wavelet Transform and
Principal Component Analysis (SWT-PCA). Accuracy image output
increases when implemented this technique and that was done by sampling
down eliminating where effects blurring and artifacts doesn't influenced.
The algorithm of SWT-PCA parameters quality measurements like NCC,
MSE, PSNR, coefficients and Eigen values.The advantages significant of this
system that provide real time, time rapid to market and portability beside
the change parametric continuing in the DWT transform. The designed and
simulation of module proposed system has been done by using MATLAB
simulink and blocks generator system, Xilinx synthesized with synthesis tool
(XST) and implemented in XilinxSpartan 6-SP605 device.
Keywords:
FPGA Xilinx system generator
Fusion image
MATLAB
SWT-PCA algorithm
VHDL
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Muthna Jasim Fadhil,
Department of Electrical Power Engineering, Electrical Engineering Technical College,
Middle Technical University (MTU),
Baghdad, Iraq.
Email: mothana.jasim@eetc.mtu.edu.iq
1. INTRODUCTION
Several domains ways of acquired information extracting are refered to fusion term where fusion
image integrating method of information relevant by images set.Fused image resultant is treated as a single
image which is suitable and high informative for processing or perception visual for separately considered
more than any original images input [1, 2]. The methods of fusion image comprise the integration of different
sources information which result high informative fused image. Image fusion is divided mainly into various
levels where in this article used fusion of pixel-level, image fusion of pixel-level technique has different
methods as Stationary Wavelet Transform (SWT), Discrete Wavelet Transform (DWT), Discrete Cosine
Transform (DCT), Principal Component Analysis (PCA) and average weighted. SWT method is important

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 757 - 766
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method technique for fusion image because of it is feature excellent and analysis frequency [3-5].
The stationary wavelet transformation is more active performance in transformation image compared with
discrete counterpart with its lossless reported, the main reason isin step down sampling of DWT and that
guide process information to discards. Because the step down sampling does not happen in SDWT so after
decomposition the approximation coefficients has the same length of original input signal and that is why
using SWT method in this articles [6-8].
Much of the research has been performed for the purpose of developing image quality but still suffer
from some problems such as principle uncertainty Heisenberg, shift-invariance lack, effects of blurring,
artifacts and noise. To addition, any image is unclear because it has a foggy and special contrast intensity and
so it will need low and high pass filters with many cut off frequencies, which increases the complexity of
the circuit more and the size of the model used. Various previous works in this field, in [9] investigate that
pyramid laplacian and method related transform wavelet using fusion image, the results explain that in both
cases Peak Signal to Noise Ratio (PSNR) increase and decrease mean square error (MSE) but it has low
edges and boundary values. In [10] represents that image obtained by various formal accord fusion image,
where decomposed input image through stratifying 2D-DWT based algorithm fusion laplacian for lower
approximation and for higher level algorithm fusion wavelet using for combined SF. The results explain that
by advocate fusion obtained image with high quality however widely decomposition of various resolution are
not invariant translation due to process sampling is down for underlying. In [11] pyramid laplacian adopted
so that it isdepending on pixel level and images of multi focused using to obtained individual image same of
Android stratify but it uses inverse filtering which will leads to change local color. In [9] investigate
transform wavelet method using fusion image by criteria smoothness and gradient combination the results
show that fusion obtained by this method is better than all the other types compared of wavelet transform but
it needs very large circuit processing so it will make extra power dissipated.
In this paper using FPGA programmed by SWT-PCA algorithm because it provides various
platforms computing such as pipelining , concurrency and reconfigure ability. The majority reason for that is
FPGA includes the main processor that provides multi benefits over the other DSPs circuits such as low cost,
low dissipated power(heat) usually under 1 watt so it can use battery as a power supply [12]. Also FPGA
architecture flexibility gives prospect making electronic system exceedingly consolidated beside that because
it’s architecture is flexible and programmability, allows to change huge amount of logic glue peripherals and
extrinsic components that is required by processor formed systems. The use of FPGA will reduce the size of
the circuit and increase its efficiency to handle a huge number of images and the extent of frequencies is very
wide by changing the cut off frequency through the programming of FPGA by VHDL which commensurate
with any algorithm and thus provides more flexibility of the circuit [13-15]. Fusion rule of SWT for Xilinx
System Generator is used in MATLAB by algorithm implementation in this paper, then code HDL is
generated by system generator finally implementation hardware modeled on FPGA Xilinx Spartan 6-SP605
with SWT-PCA algorithm applied.
2. FUSION IMAGE TECHNIQUES
The techniques of fusion image are divided into two domains frequency (spectral) and time (spatial).
PCA transform variables correlated number into variables uncorrelated,so that,to become more reliable and
accurate. Further that dimensions number are reduced by selecting Eigenvector value of high order as
component principle for high rate computation results. It is method of rigorous quantitatively that new group
of variables is generated called essential components where most of these components are perpendicular to
each other which guide to alleviate information redundant. PCA has widely applications such as matching
pattern, wireless communication, machine learning and image processing.
When output image quality poor due to effects blurring [16-18], artifacts and distortionresults from
shift-invariance lack because of DWT down sampling. Resolving these issues are done by down sampling
removing using SWT so, conserve output imagewith high informative and quality. Decomposed original
image into approximation vertical and horizontal by applying high pass and low pass filters wise row and
wise column. Figure 1 elaborates detail of diagonal, horizontal, vertical and approximate result from wise
column and wise row decomposed by same filtration. High and low pass filters conserve high and low
frequencies beside information detailed provides at respective frequencies [19-21]. The following are SWT
equationsof decomposition wavelet,
𝑅𝑖, 𝑠1, 𝑠2 = ∑ ∑ 𝑝0
↑2𝑖
𝑚2𝑚1 (𝑚1 − 2𝑠1)𝑝0
↑2𝑖(𝑚2 − 2𝑠2)𝑅𝑖−1, 𝑚1, 𝑚2 (1)
Wi
1
,s1, s2 = ∑ ∑ p0
↑2i
m2m1 (m1 − 2s1) f0
↑2i(m2 − 2s2)Ri−1, m1,m2 (2)

Int J Elec & Comp Eng ISSN: 2088-8708 
Design and implementation a prototype system for fusion image by using SWT.... (Muthna Jasim Fadhil)
759
Wi
2
, s1, s2 = ∑ ∑ f0
↑2i
m2m1 (m1 − 2s1) p0
↑2i(m2 − 2s2)Ri−1, m1, m2 (3)
Wi
3
, s1, s2 = ∑ ∑ f0
↑2i
m2m1 (m1 − 2s1) f0
↑2i(m2 − 2s2)Ri−1, m1, m2 (4)
Where Wi
3
, s1, s2; Wi
2
,s1, s2; Wi
1
,s1, s2 andRi, s1, s2 are components diagonal (HH), high frequency for
Vertical (HL), high frequency for horizontal (LH) and low frequency (LL) for SWT respectively.
i = 0,1,2,…..i-1 is level decomposition with p0
↑2i
𝑎𝑛𝑑 f0
↑2i
are added zeros indicated by 2i-1.
Figure 1. The explaination diagram for processing of decomposition image that using SWT method
The reconstructed SWT is shown in (5):
Ri−1, m1, m2 =
1
4
∑ (∑ ∑ p1(m1s2s1
3
j=0 − 2s1 − j)p1(m2 − 2s2 − j)Ri, s1, s2 +
∑ ∑ p1(m1s2s1 − 2s1 − j)f1(m2 − 2s2 − j)wi
1
,s1, s2 +
∑ ∑ f1(m1s2s1 − 2s1 − j)p1(m2 − 2s2 − j)wi
1
,s1, s2 +
∑ ∑ f1(m1s2s1 − 2s1 − j)f1(m2 − 2s2 − j)wi
2
, s1, s2 ) (5)
When average and median filtersare combined to give performance better for reduction noise and resizes,
adaptively the mask filter is depending on mask noise level. The noise is reduced by this technique and detail
image will be better by conserve edges.In part of reduction noise, each pixel check sequentially,
if the average value is less than the pixel value that is means noise affected on pixel and mask median value
replace the pixel otherwise pixel value unchanged. The eigen values can be calculated by the following
equation:
Det|Ri − γI| = 0 (6)
Where γ is the Eigen values.To calculate the co-ordinates that have component principal direction for all data
points by (7):
𝐷𝑉𝑖 = 𝑟𝑗1 𝐵1 + 𝑟𝑗2 𝐵2 … … … . 𝑟𝑗𝑚 𝐵 𝑚 (7)
Where rj is factor for j coefficient, DViis ith component principal and B1, B2………….Bm are each data
co-ordinate [22-24].
2.1. SWT-PCA algorithm proposed
The proposed method has two steps: at stage preprocessing, filter average and median combined
enforce on input images then SWT-PCA hybrid is using for fusion image. The steps below explain stage
of preprocessing.
Step 1: mask resize adaptively:
Filter initialize by m=3.
Evaluate R1 = MED − MIN,R2 = MED − MAX
Check if R1 > 0 𝑎𝑛𝑑 R2 < 0 then go to step1 otherwise the mask size enlarge by m=m+2.

 ISSN: 2088-8708
760
Where m, R1, MAX, MIN and MED represent size of the mask,average value, maximum, minimum and
median respectively.
Step 2: The values median are evaluated by using filtering median.
The output imagesK1 and K2 are gained when input images enforce by additional filters SWT-PCA
hybrid, K1 and K2 images are used to sub bands decomposed HH1, HL1, LH1, LL1 and HH2, HL2, LH2,
LL2. The maximum values of Eigen vectors and sub band are calculated by using PCA. Sub bands are
mentioned and combined to sum and multiply each images sub bands. The following equations represent
the calculation of new subbands as HHnew, HLnew, LHnew, LLnew [25-27]:
𝐿𝐿 𝑛𝑒𝑤 = 𝐷𝑉1 × 𝐿𝐿1 + 𝐷𝑉2 × 𝐿𝐿2 (8)
𝐿𝐻 𝑛𝑒𝑤 = 𝐷𝑉3 × 𝐿𝐻1 + 𝐷𝑉4 × 𝐿𝐻2 (9)
𝐻𝐿 𝑛𝑒𝑤 = 𝐷𝑉5 × 𝐻𝐿1 + 𝐷𝑉6 × 𝐻𝐿2 (10)
𝐻𝐻 𝑛𝑒𝑤 = 𝐷𝑉7 × 𝐻𝐻1 + 𝐷𝑉8 × 𝐻𝐻2 (11)
Where DV1, DV2 … … … … … DV8 are sub bands components principal.
The algorithm of hybrid SWT-PCA involved in Figure 2 where the average and median filters
combined are enforced to input images where the filter output is loaded to wise row and wise column of high
and low pass filters which are designed, simulated and implemented in FPGA.SWT Haar applying to
obtained sub bands by decomposedthem. PCA is usedfor evaluation resultant coefficients and each image
source coefficient enforce to denoted PCA coefficients, components PCA merges fusion rule to new
coefficients decomposed (HHnew, HLnew, LHnew and LLnew). The transform of combined coefficients is
loaded to inverse SWT in order to obtain image fusion.Finally, fusion image that is processing in FPGA and
MATLAB with high informative and quality display on computer. Most of the algorithm in SWT-PCA
was implemented in Field Programmed Gates Array (FPGA) Xilinx Spartan6-SP605 deviseto gain high
accuracy and speed in data processing also in order to save power and using small size of the proposed
electronic circuit.
Figure 2. Flow chart of the proposed method

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3. FPGA IMPLEMENTATION HARDWARE
The algorithms of fusion image processing and implementation on hardware are considered as
the most achievable method for systems performance improving. FPGA hardware reconfigurable features is
superior offering compare with DSP and other devices hardware because of reliability product and
advantages maintainability in processing of digital image. Many algorithms require sets of multiple data
processing that sequentially performed in FPGA and on computer [14]. The approximate information and
details of the image decomposed by using low pass and high pass filter respectively, this scheme of
extrapolation turns into very simple when implemented in hardware. High pass and low pass filters designed
and implemented in Xilinx System Generator as shown in Figure 3. The process of decomposition can
be repeated with approximations successive where many components with less resolution existence
are decomposed.
Figure 3. High pass and low pass filters design and implemented in FPGA Xilinx system generator
Schematic diagram of based on algorithm of fusion image is shown in Figure 4, module design in
simulnk completed for fusion image that implemented on FPGA Xilinx Spartan6-SP605 device and using
SWT-PCA algorithm are shown in Figure 5. The images store in block input buffer after that send for
processing into block of fusion that information receives of wise pixel and using BRAM to store them.
Each pixel is red by RAM dual port additional that all pixels display through connector output, the file
programming using VHDL that can be created by Xilinx ISE. System generator simulation is using to
generate block for co-simulation hardware model that has been performed by JTAG.
Figure 4. Block diagram of SWT-PCA algorithm for fusion image

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These modules synthesis by files net list creationpriorwhich represent module implementation input.
The next stage of files generation is that design logic transform to a bit file and using FPGA for downloaded.
The system model in Figure 5 details as below:
Step 1 : From file of images load sample 1 and sample 2 imagesbefore transform into scale of gray by using
MATLAB then transform to matrix format.
Step 2 : Combine average and median filters to obtain output images K1 and K2 and resize both images to
be 512*512 or 256*256.
Step 3 : Apply low pass and high pass filters to each column and row.
Step 4 : The proposed model simulink of SWT rule maximum fusion based on SWT-PCA algorithm is
improved in MATLAB and FPGA Xilinx Spartan6-SP605.
Step 5 : Xilinx System Generator (XSG) is inserted and using for coding VHDL compileand bit filecreation
then send it to FPGA.
Step 6 : SRAM is using to store FPGA output then using MATLAB for postprocessing.
Step 7 : SRAM output is loaded to MATLAB in order to image fusion output analysis.
Figure 5. Modeling simulink of fusion image using SWT-PCA algorithm applied in FPGA
SWT-image block internal design is shown in Figure 6 contains of two stages, in first stage of SWT
with one level, all image rows transform again utilize horizontally filter bank take in consideration
decomposition of first level produces images sub-sampled and several number of 4 filtered. In decomposition
of second level, further SWT sub bands of lowest divided using same above method of filtering, sub band
lowest further decomposed to 4 sub bands where each row and columns of sub band lowest send to
fusion block.

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Figure 6. Internal design of SWT-img block that using in Figure 5 for system proposed model
4. RESULTS AND DISCUSSION
The Boolean expression optimization in FPGA is taken in consideration speed and area of
processing here optimization of delay and area occurred by implementation of SWT-PCA algorithm where
segments wire of FPGA programmable are routing through blocks FPGA among connection established.
The utilization device contains the following: utilization and distribution logic, Figure 7 represents report
synthesis the proposed system which is implemented in FPGA.
Figure 7. Synthesis report of system proposed implemented on FPGA
The functionality of SWT is verified and simulated by their development that is done when
the model RTL is sent to the process of synthesis using tool ISE of Xilinx as shown in Figure 8. In process of
synthesis converted model RTL to the mapped net list level gate then to library of technology, the results and
synthesis of SWT design have been analyzed. The results of simulation models in the proposed design are
presented in the diagram of Figure 9 where MATLAB using input VHDL for processing actually the file of
input image. The LL-coefficients contain image compressed that was gotten when applied SWT while the
reverse applied by using decompressing method. Where the file input is represented by image compressed
and VHDL using for doing ISWT.

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Figure 8. RTL schematic diagram in Xilinx FPGA system design
Xilinx platform is including code VHDL that using for the simulation waveform of coefficient
extracted as shown in Figure 9. The calculation of coefficients have been done before and after fused image
by using SWT-PCA method. The contrast is improved and high visibility provides by fused image, samples
image individually of information integrated. The processing and diagnosis accurate can be done for injuries,
tumour and cancer with high resolution and contrast by using fusedimage. The images fusing technique used
the algorithm of SWT-PCA for different processing stages and these images Eigen values and coefficients
are denoted as advance case.
Figure 9. Simulation waveform of propoed system that implemented in FPGA
Database images that using in Table 1 for analysis are taken different images for several patients,
these images using for fusion further calculated Eigen values and coefficients as shown in Table 1.
SWT-PCA using for adopting the method of fusion image, the Eigen values and coefficients are listed
in Table 1.

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Table 1. Different medical images with their Eigen values and coefficients of fused one
Sl.no. Ref.[11] Image
sample1 CT scan
This work Fused image Coefficient Eigen
value
Coefficient Eigen
valueImage sample2 MRI
1. 0.5892
- 0.8183
- 0.8183
- 0.5892
0.3203
7.839
0.5923
- 0.8102
-0.8102
-0.5923
0.4651
8.7331
2. -0.9532
0.2357
0.2357
0.9532
0.6358
5.7663
-0.8853
0.3177
0.3177
1.1153
0.7531
5.9447
3. -0.7947
0.7654
0.7654
0.7947
1.2777
5.0938
-0.7092
0.8332
0.8332
0.8092
1.8232
5.4377
4. - 0.8367
0.5105
0.5105
0.8367
0.7731
5.7352
-0.8244
0.6211
0.6211
0.8544
0.8127
5.9164
5. 0.5414
- 0.8712
- 0.8712
- 0.5414
0.3601
7.3421
0.5721
-0.8687
-0.8687
-0.5721
0.4722
7.8347
6. -0.9115
0.2844
0.2844
0.9115
0.6167
5.3125
-0.8902
0.3102
0.3102
0.8902
0.6678
5.7551
7. -0.7149
0.7981
0.7981
0.7149
1.1057
5.1443
-0.6922
0.8203
0.8203
0.6922
1.3655
5.7342
8. - 0.8233
0.4443
0.4443
0.8233
0.6121
5.6854
-0.8115
0.6281
0.6281
0.8115
0.6733
5.8402
5. CONCLUSION
Image fusion tempts a lot of attention specialist in the vision of computer and sensing of remote
fields while in various domains of applications the processing real time tackle presented also in
implementations of hardware. SWT-PCA technique is applied here for better resolution and contrast of image
fusion additional that images corners are detected using Edge detection, image fusion is the best way to
diagnose and treat diseases, especially in the early stages. Images samples are taken and processing using
SWT-PCA algorithm to reinforce the image with better contrast. Employed fusion image is taken in order to
obtain information mutual and common using information better equality for each image, also contrast and
resolution better result.
The image information concern such as Eigen values and coefficients that are bring from individual
image through group of images using tools of MATLAB. From results of Table 1 the Eigen values and
coefficients that obtained from fusion of MRI and CT Brain Images in this paper are more better than those
parameters are registered in Ying et al. so the fusion image results with higher resolution and contrast.

 ISSN: 2088-8708
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Fused image is extracted pixels along with coefficients and carried out in Field programmable
Gate Array Xilinx Spartan6-SP605 device by taking advantage of analyzed power and area reduction.
The technology of FPGA provides low power, compact and fast solution of fusion image. The FPGA
image implementation can be utilizing in the domain of medical applications and results with high
quality resolution.
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