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\title{Feature-Level Multi-Focus Image Fusion Using Neural Network and Image Enhancement}
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             \author[1]{Shaik Abdul  Rahim}

             \author[2]{Dr. G.  Mamatha}

             \author[3]{Cyril Prasanna  Raj}

             \affil[1]{  J.N.T University-Anantapur-India.}

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\date{\small \em Received: 6 February 2012 Accepted: 4 March 2012 Published: 15 March 2012}

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\begin{abstract}
        


Image Processing applications have grown vastly in real world. Commonly due to limited depth of optical field lenses, it becomes inconceivable to obtain an image where all the objects are in focus. Image fusion deals with creating an image where all the objects are in focus. After image fusion, it plays an important role to perform other tasks of image processing such as image enhancement, image segmentation, and edge detection. This paper describes an application of Neural Network (NN), a novel feature-level multifocus image fusion technique has been implemented, which fuses multi-focus image using classification. The image is divided into blocks. The block feature vectors are fed to feed forward NN. The trained NN is then used to fuse any pair of multi-focus images. The implemented technique used in this paper is more efficient. The comparisons of the different existing approaches along with the implementing method by calculating different parameters like PSNR,RMSE.

\end{abstract}


\keywords{Multi-focus image fusion, feed forward neural network, image Enhancement.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
mage fusion utilises information obtained from a multi-focus images of the same scene. Image processing is one of form of signal processing for which the input is an image and the output of image processing may be either an image or a set of characteristics related to the image. For the most of the image processing techniques, images of two dimensional signals is treated as input and standard image processing techniques are applied to it. The process of image fusion is performed for multi-focus and multi-sensor images of the same scene. In multifocus images, the physical objects in the scene which are closer to the camera are in focus and the far physical object gets blurred. Adverse to it, when the far physical objects are focussed then the closer objects get blurred in the image. A hierarchical idea of image fusion has been implemented for combining significant information from multiple images into single image. The process of image fusion can be accomplished either in transformed domain or spatial domain. In spatial domain operations are performed on the pixel values. In transformed domain the images are first transformed into multiple levels of resolutions.\par
Information fusion can be performed at any level of the image information representation corresponding to other forms of information fusion, image fusion is usually performed at one of the three different processing levels they are Pixel, Feature and Decision Level \hyperref[b4]{[5]}. The pixel level image fusion is also known as signal level image fusion which represents fusion at the lowest processing level, that is operations such as maximum or mean(average) are applied to the pixel values of the source images to generate the fused image. Feature level image fusion is also known as object level image fusion where fused features and object labels and information that have already extracted from individual input images. Decision level is also known as symbol level, the objects in the input image are first detected and then the suitable fusion algorithm the fused image is generated. In the field of Image Processing, image fusion has received a significant importance for medical imaging, military applications, forensic, remote sensing.\par
A number of image fusion techniques have been exhibited in the literature. In addition of simple pixel level image fusion techniques. We find the complex techniques such as Laplacian Pyramid \hyperref[b1]{[2]}, Morphological pyramid \hyperref[b5]{[6]}, fusion based on PCA \hyperref[b2]{[3]}, Discrete wavelet Transform (DWT) \hyperref[b0]{[1]}. These fusion techniques have different advantages and disadvantages such as liner wavelets during image decomposition the fused image doesn't preserve the original data. Likewise due to low-pass filtering of wavelets, the edges in the image becomes smooth and hence the contrast in fused image is decreased.\par
In this paper, we have implemented a method for multi-focus image fusion. The implemented method is discussed in section II. In section III, the quantitative measures used to evaluate the performance of the implemented method are described. Section IV covers the experiments details and section V concludes the study. 
\section[{II.}]{II.} 
\section[{Implementing method}]{Implementing method}\par
Different images are acquired from the Image Processing websites. From the acquired images consider one image, for that image generate two source images from original image that is one is left focused and right blurred other one right focused and left blurred. Every image is divided into blocks. The block size plays a significant role in differentiating the blurred 2) Spatial Frequency: Spatial frequency measure the activity level in an image, it used to calculate the frequency changes along rows and columns of the image. Spatial frequency is measured using equation (  {\ref 2}).\par
(2)\par
Where and Here X is the image and p*q is the image size. A large value of spatial frequency describes the large information level in the image and therefore it measures the clearness of the image.\par
3) Variance: Variance is used to measure the extent of focus in an image block. It is calculated using equation (  {\ref 3})(3)\par
Here is the mean value of the block image and p*q is the image size. A high value of variance shows the greater extent of focus in the image block.   ( D D D D ) ( , ) 1 ( , ) * k K i j Bk X i j CV p q ? ? ? ? ? K ? ( ) \textasciicircum 2 ( ) \textasciicircum 2 SPF RF CF ? ? 1 2 1 [ ( , ) ( , 1)] \textasciicircum 2 * pq i j RF X i j X i j pq ? ? ? ? ? ?? 1 2 1 [ ( , ) ( 1, )] \textasciicircum 2 * pq i j CF X i j X i j pq ? ? ? ? ? ?? 1 1 1 ( ( , ) ) \textasciicircum 2 * p q i j Variance X i j pq ? ? ? ? ? ?? ? 
\section[{Where and}]{Where and}\par
Here p and q represent the dimensions of the image block. A high value of energy of gradient shows greater amount of focus in the image block.\par
5) Edge Information: The edge pixels can be found in the image block by using canny edge detector. It returns 1 if the current pixel belongs to some edge in the image otherwise it returns 0. The edge feature is just the number of edge pixels contained within the image block. 
\section[{c) Artificial Neural Networks}]{c) Artificial Neural Networks}\par
Many Neural Network models have been implemented for tackling a diverse range of problems*, including pattern classification. The fusion we examine here can be considered as classification problem. Here we have considered a NN applications model, namely the PNN (Probabilistic Neural Network) .The basic idea underlying NN is to overlap localized receptive fields of the hidden units to create arbitrarily complex nonlinear ties. The normal architecture consists of one hidden layer and one output layer. Each hidden unit corresponding to a kernel or basis function of the input vector x, and is usually of the Gaussian form. The basic architecture of feed forward NN is shown below Here, c is the position of the hidden unit and is a user-defined width that controls is spread. For PNN a hidden unit is positioned at every training data point. 
\section[{d) Neural Network Algorithm}]{d) Neural Network Algorithm}\par
The algorithm first decomposes the source images into blocks. Given two of these blocks (one from each source image), a neural network is trained to determine which one is clearer. Fusion then proceeds by selecting the clearer block in constructing the final image. The fusion result of DWT is shift dependent. The use of image blocks on the other hand, avoids this problem even if there is object movement or misregistration in the source images, each object will still be in better focus in one of the source images. In detail, stepwise working of the implemented method is given under. 1) LFi is the left-focused and RFi is the rightfocused versions of the ith image in the dataset in section(II-A). 2) Divide the versions LFi and RFi of every image in the dataset into k number blocks of the size M*N. 3) Create the features file for all LFij and RFij according to the features discussed in section (II-B). Here j=1,2,3...k. For all i, there are two sets of features values for every block j named as FSLFij and FSRFij each of which contains five feature values. Subtract the features values of block j of RFi and include this pattern in feature file. Normalise the feature value between [0 1]. 4) Assign the class value to every block j of ith image. If block j is visible in LFi then assign it class value 1 otherwise give it a class value -1. In case of class value -1, block j is visible in RFi. 5) Train a neural network to determine whether LFi or RFi is clearer. Identify the clearness of all the blocks of any pair multi-focus images to be fused. 6) Fuse the given pair of multi-focus images block by block according to the classification results of the neural network. Such that Output of NN for block J If>0,select J from left-focused Image If<0,select j from right-focused Image The block diagram of the implemented method is shown in figure \hyperref[b1]{(2)}. 
\section[{III.}]{III.} 
\section[{Quantitative measures}]{Quantitative measures}\par
There are different quantitative measures which are used to evaluate the performance of the fusion techniques. These are PSNR (Peak Signal to noise ratio), RMSE (root mean square error), Entropy, Correlation Coefficient, MAE (mean absolute error). Here R, F are the reference and fused images respectively.p*q is the image size. 
\section[{c) Entropy}]{c) Entropy}\par
Quantifies the quantity of information contained in the fused image. A bigger value shows good fusion results.( D D D D ) 1 1 1 1 ( \textasciicircum 2 \textasciicircum 2) p q i j i j EG r r ? ? ? ? ? ? ?? ( 1, ) ( , ) i r r i j r i j ? ? ? ( , 1) ( , ) j r r i j r i j ? ?? ( ) exp( ) \textasciicircum 2/ \textasciicircum 2 Z x x c ? ? ? ? 1 1 \textasciicircum 2 10 1 ( ( , ) ( , ))\textasciicircum 2 * 20 log p q i j L R i j F i j p q PSNR ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? 1 [ ( , ) ( , )] \textasciicircum 2 * pq ij RMSE R i j F i j pq ?? ?? ?? 1 2 0 ( ) log ( ) L F F i H h i h i ? ? ?? ?\par
Here is the normalized histogram of fused image and L is the number of gray levels. 
\section[{d) Correlation Coefficient}]{d) Correlation Coefficient}\par
The correlation coefficient matrix represents the normalized measure of the strength of linear relationship between variables.\par
Where is a data value at time step t, k is the lag. 
\section[{e) MAE}]{e) MAE}\par
It is used to calculate the mean absolute error between reference image and fused image.\par
Where the predicted is fused image and is the true value fused image.  
\section[{Experiments and results}]{Experiments and results}\par
Image fusion is performed. The implemented technique used in this paper is more efficient and useful, to highlight the efficiency. We have performed broad experimentation on this technique. We trained the feed forward neural network with different number of hidden layers and with different number of neurons on each layer  High performance.( ) \textasciicircum 2 N t t k t N t t x x x x CC x x ? ? ? ? ? ? ? ? ? ? ? ? F h t x 11 1 ( , ) ( , ) * pq ij MAE R i j F i j pq ?? ?? ?? ( , ) F i j ( , ) R i j\textbf{)}\par
Texture Calculations:\par
V. 
\section[{Conclusion}]{Conclusion}\par
In this implemented technique, a feature level focus image fusion has been implemented in this paper. In this method we have trained the feed forward neural network with the block features of pairs of multi-focus images. A feature set including SF, CV, edges, variance, and EG is used to define clarity of the image block. The trained neural network was then used to fuse any pair of multi-focus images. Experimentation results show that the implemented technique performs better than the existing techniques. The fusion result of Discrete Wavelet Transform is shift dependent. The use of image block, on the other hand avoids the problem of shift dependent.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}F}\end{figure}
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\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 1 :}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
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\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
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  {\small\itshape [Note: © 2012 Global Journals Inc. (US) Global Journal of Computer Science and Technology Volume XII Issue X Version I 18]} 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
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21\\
D D D D )\\
(\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_3}.80 4.635 7.2913 0.9966 0.0104Table 3 :}\end{figure}
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\end{document}
