A comparison between scilab inbuilt module and novel method for image fusion
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This paper compares a novel mathematical method for image fusion with the inbuilt module of Scilab, demonstrating that the novel method yields clearer and more effective images. The study highlights that the novel approach, independent of any theoretical framework, offers superior performance in image clarity compared to the traditional Scilab method. The results affirm the feasibility and validity of the proposed method for generating high-quality fused images.
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![Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 2, April 2014
1
A Comparison between Scilab inbuilt module and Novel
method for image fusion
By
Deepali Kaushik, Himanshu Jain
KIET Ghazibad, India
NIT Meerut , India
Kaushikdeepali61@gmail.com, himanshunitcs@gmail.com
ABSTRACT
Image fusion is one of the important embranchments of data
fusion. Its purpose is to synthesis multi-image information in
one scene to one image which is more suitable to human
vision and computer vision or more adapt to further image
processing such as target identification.
This paper mainly compares the Scilab inbuilt module and
novel method for image fusion. By using scilab as
experimental platform, we approved the feasibility and
validity of method. The result indicate that the fused image
quality would be very effective and clear.
Keywords
Image fusion, multi-image, image processing.
1. INTRODUCTION
The term fusion means in general an approach to extraction of
information acquired in several domain [1]. To produce the
fused image we need several images of the same scene. It
means we need at least two image as input in which the size
of image should be same. Then the output (fused image) will
be in one image of higher quality. We need image fusion in
image processing because it requires both high spatial and
high spectral information in one image. The goal of image
fusion is to integrate complementary multisensor,
multitemporal and multiview information into one new image
containing information the quality of which cannot be
achieved otherwise [2].
In this paper we will compare the scilab in-built module to
novel method for fused image. In scilab, there is no need to
create the code by us for image fusion because it is inbuilt
module. In novel method, it is created by us which is
completely based on mathematical concept. Then we will
compare both method with their perspective result. Image
fusion based novel method implemented in Scilab.
Figure 1: Image Fusion Method
2. LITERATURE SURVEY
As we studied various methods for image fusion that the
statistical methods of pixel based image fusion techniques
produced fused image on the basis of pixel value. Regression
variable substitution (RVS) produced better result than other
methods [3][4]. Wavelet transform and multi-wavelet
transform method provide high quality spectral content but
these methods are dependent on theory [5]. Novel method is
independent to any type of theory, it is completely
mathematical concept based. It is easy to implement than any
other method. It is very simple and easy to understand than
other methods. It produced efficient and high quality of
clearly visible image.
3. NOVEL METHOD
This paper we define novel method. It is completely
mathematically based method. We did not use any theory or
application in novel method. It is very simple to implement.
This method is used to image fusion in easy way. There is
need at least two image in which the size of image should be
same. Using the code of this method generates appropriate](https://image.slidesharecdn.com/acomparisonbetweenscilabinbuiltmoduleandnovelmethodforimagefusion-140504123643-phpapp01/85/A-comparison-between-scilab-inbuilt-module-and-novel-method-for-image-fusion-1-320.jpg)
![Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No. 2 Issue No. 2, April 2014
3
5. CONCLUSION
Novel method is very effective method to produce the good
pixel image. This paper provides a novel method for image
fusion which is very effective and independent to any theory.
The result show in image c) that is provide better performance
and clearity than other fusion method.
6. REFERENCES
[1] Haozheng Ren, Yihua Lan, and Yong Zhang “Research of
multi-focus image fusion based on M-band multi-wavelet
transformation” IEEE Fourth International Workshop on
Advanced Computational Intelligence Wuhan, Hubei, China;
October 19-21, 2011.
[2] Wang Enjun “research on road image fusion enhancement
technique based on wavelet transform”
IEEE Vehicle Power and Propulsion Conference (VPPC),
September 3-5, 2008, Harbin, China
[3] Deepak kumar sahu, M.P.parsai “different image fusion
techniques” international journal of modern engineering
research (IJMER) Vol. 2, Issue. 5, Sep-Oct 2012 pp-4298-
4301 ISSN: 2249-6645
[4] Ali A. AI-Zaky “The statistical methods of pixel- based
image fusion techniques” international journal of artificial
intelligence and knowledge discovery” Vol. 1, Issue 3 12 Aug
2011.
[5] Mirajkumar pradnya P, Sachin D. “image fusion method
based on WPCA” international journal of advanced research
in computer science and software engineering Vol. 2, Issue 5,
May 2012.](https://image.slidesharecdn.com/acomparisonbetweenscilabinbuiltmoduleandnovelmethodforimagefusion-140504123643-phpapp01/85/A-comparison-between-scilab-inbuilt-module-and-novel-method-for-image-fusion-3-320.jpg)
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1. Decomposing the input images into base and detail layers using simple average filtering.
2. Generating guided weight maps for the base and detail layers of each input image using saliency maps and guided filtering.
3. Reconstructing the fused image by weighted summation of the base and detail layers using the guided weight maps.
The proposed method aims to preserve edge information better than other methods by exploiting spatial context with guided filters during the fusion process. It is compared to other methods based on quality assessment results.
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2. It proposes decomposing images using a contourlet transformation with modified directional filter banks that incorporate log Gabor filters. This aims to provide high quality fused images while localizing features accurately and minimizing noise.
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Multimodal Medical Image Fusion Based On SVD
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A comparison between scilab inbuilt module and novel method for image fusion
- 1. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 2, April 2014 1 A Comparison between Scilab inbuilt module and Novel method for image fusion By Deepali Kaushik, Himanshu Jain KIET Ghazibad, India NIT Meerut , India [email protected], [email protected] ABSTRACT Image fusion is one of the important embranchments of data fusion. Its purpose is to synthesis multi-image information in one scene to one image which is more suitable to human vision and computer vision or more adapt to further image processing such as target identification. This paper mainly compares the Scilab inbuilt module and novel method for image fusion. By using scilab as experimental platform, we approved the feasibility and validity of method. The result indicate that the fused image quality would be very effective and clear. Keywords Image fusion, multi-image, image processing. 1. INTRODUCTION The term fusion means in general an approach to extraction of information acquired in several domain [1]. To produce the fused image we need several images of the same scene. It means we need at least two image as input in which the size of image should be same. Then the output (fused image) will be in one image of higher quality. We need image fusion in image processing because it requires both high spatial and high spectral information in one image. The goal of image fusion is to integrate complementary multisensor, multitemporal and multiview information into one new image containing information the quality of which cannot be achieved otherwise [2]. In this paper we will compare the scilab in-built module to novel method for fused image. In scilab, there is no need to create the code by us for image fusion because it is inbuilt module. In novel method, it is created by us which is completely based on mathematical concept. Then we will compare both method with their perspective result. Image fusion based novel method implemented in Scilab. Figure 1: Image Fusion Method 2. LITERATURE SURVEY As we studied various methods for image fusion that the statistical methods of pixel based image fusion techniques produced fused image on the basis of pixel value. Regression variable substitution (RVS) produced better result than other methods [3][4]. Wavelet transform and multi-wavelet transform method provide high quality spectral content but these methods are dependent on theory [5]. Novel method is independent to any type of theory, it is completely mathematical concept based. It is easy to implement than any other method. It is very simple and easy to understand than other methods. It produced efficient and high quality of clearly visible image. 3. NOVEL METHOD This paper we define novel method. It is completely mathematically based method. We did not use any theory or application in novel method. It is very simple to implement. This method is used to image fusion in easy way. There is need at least two image in which the size of image should be same. Using the code of this method generates appropriate
- 2. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 2, April 2014 2 result than other method. Now we will explain novel method in terms of code. Firstly we will take two image as named im1(a) and im2(b). The size of im1 and im2 should be same as well as of same scene after that we will take 3 zeros values according to the size of both image. fus=zeros(a,b,c) Where a, b, c are the pixel value Then image would be executed in three level ratio as- For i=1: a For j=1:2: b For k=1:3 The first image im1(i,j,k)is copy into fus(i,j,k). The same procedure is apply for second image, finally it will produced fused image as- imshow(fus) Figure 2: Im1 Figure 3: Im2 Figure 4: Fused image using novel method Finally this novel method produce fused image c) as we can see this image seen clearly and effective. 4. CAMPARISION BETWEEN SCILAB MODULE AND NOVEL METHOD Scilab module which is inbuilt, there is no need to create the code to generate fused image .in this method also we will take two image in the same scene as well as the size of image should be same. Scilab module used imadd() to generate fusion mage which is inbuilt method. It produced fused image randomly as seen in image d). But this image is not seen clearly than using novel method. The second image im2 b) is not shown clearly and the colour of fused image change. Even it change the background colour. We can see image d) image is not shown properly and look like a blurred image. As we can see both fused image c) and d) and distinguish the property of the fused image on its own method. Figure 5: Fused image using scilab inbuilt method
- 3. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804) Volume No. 2 Issue No. 2, April 2014 3 5. CONCLUSION Novel method is very effective method to produce the good pixel image. This paper provides a novel method for image fusion which is very effective and independent to any theory. The result show in image c) that is provide better performance and clearity than other fusion method. 6. REFERENCES [1] Haozheng Ren, Yihua Lan, and Yong Zhang “Research of multi-focus image fusion based on M-band multi-wavelet transformation” IEEE Fourth International Workshop on Advanced Computational Intelligence Wuhan, Hubei, China; October 19-21, 2011. [2] Wang Enjun “research on road image fusion enhancement technique based on wavelet transform” IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China [3] Deepak kumar sahu, M.P.parsai “different image fusion techniques” international journal of modern engineering research (IJMER) Vol. 2, Issue. 5, Sep-Oct 2012 pp-4298- 4301 ISSN: 2249-6645 [4] Ali A. AI-Zaky “The statistical methods of pixel- based image fusion techniques” international journal of artificial intelligence and knowledge discovery” Vol. 1, Issue 3 12 Aug 2011. [5] Mirajkumar pradnya P, Sachin D. “image fusion method based on WPCA” international journal of advanced research in computer science and software engineering Vol. 2, Issue 5, May 2012.