Ijctt v9p109

International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 1 – Mar 2014

Facial Image Noise Removal Via a Trained Dictionary Sudharson.D1, Kavinraj.A.S 2, Sridhar.S 3, C.Dinesh Kumar 4

(1PG SCHOLAR, Sri Krishna College Of Engineering And Technology, Coimbatore, India.)

(2PG SCHOLAR, Sri Krishna College Of Engineering And Technology, Coimbatore, India.)

(3PG SCHOLAR, Sri Krishna College Of Engineering And Technology, Coimbatore, India.)

(4PG SCHOLAR, Sri Krishna College Of Engineering And Technology, Coimbatore, India.)

ABSTRACT: In this project we address that, sparsity has shown to be useful in source separation. In most cases, the sources are not sparse currently and needs to sparsify them using a known dictionary. The problem here is that, if the sparse domain is not available then it will be difficult to recover the source using the current algorithms. In-order to address this problem we fuse the dictionary into the source separation. We define a cost function based on the idea and propose by extending the de-nosing method and minimize it.

The term sparse refers to signals or images with small number of non - zeros with respect to some representation bases. In sparse component analysis (SCA), the assumption is that the sources can be sparsely represented using a known common basis or dictionary. The existing system defines that the

1.INTRODUCTION Blind source separation by Independent Component Analysis (ICA) has received attention due to its potential application in signal processing. The goal of ICA is to recover the independent sources[1] given only sensor observations that are unknown linear mixtures of the unobserved independent source signals. It also assumes that the sources are nonGaussian and separate them by minimizing the mutual information. Non-negativity is another constraint used for source separation. In non-negativity factorization, the matrix is decomposed into product of two matrices, allows additive combination and produces a part based representation of data[2]. ICA is a technique to solve the blind source separation. BSS is based on the assumptions that source signals are independent with each other. Sparse coding is a method for finding suitable representation of data in which the components are rarely active. It has been shown that this sparse representation can be used to solve the BSS problem.

ICA algorithms like FASTICA[3], uses kurtosis as a sparseness measure and since kurtosis is sensitive to the outliers as it applies more weight on heavy tails rather than on zero, this measure is mostly unreliable. When the sources are locally very sparse the matrix identification algorithm is much simpler. A simpler form, for separation of mixtures from images after sparsification transformation is hence used.

2.ABOUT THE PROJECT Blind source separation (BSS) is the process of extracting the underlying sources called Source Separation from the mixed images or observed signals, and since no a priori knowledge of the mixed sources is known or very little information is available, it is called blind. Independent

ISSN: 2231-2803

techniques like MCA which is used provide a noisy mixture and present the source. In the proposed technique FastICA algorithm which employs a modified Gaussian for blind source separation. The proposed non-linear function which is used to separate image mixtures and result in faster execution and in good quality image separation.

Keywords--Blind Source Separation(BSS), Dictionary Learning, Independent Component Analysis(ICA),Morphological Component Analysis(MCA), Non-Linear Functionality.

component analysis (ICA) is most widely used technique to solve the blind source separation problem. BSS is based on the assumptions that source signals are independent with each other. Sparse coding is a method for finding suitable representation of data in which the components are rarely active. It has been shown that this sparse representation can be used to solve the BSS problem. ICA algorithms i.e., FASTICA uses kurtosis as a sparseness measure and since kurtosis is sensitive to the outliers as it applies more weight on heavy tails rather than on Zero, this measure is mostly unreliable. When the sources are locally very sparse the matrix identification algorithm is much simpler. A simpler form, for separation of mixtures from images after sparsification transformation is hence used.

In existing system they gave a solution via fusing the dictionary learning into the source separation. k- SVD algorithm described that was extended to multichannel signals.

This algorithm tries to compute a set of patterns M and a sparse approximation of the signal on the generated dictionary. They adapted MMCA[5] to those cases that the sparsifying dictionaries/transforms are not available. The existing algorithm was designed to adaptively learn the dictionaries from the mixed images within the source separation process. This method is motivated by the idea of image denoising using a Trained dictionary from the corrupted image. A dictionary can be Trained to optimize sparse signal representations for inverse problems. Despite the lack of mathematical results, numerical calculations results show that such algorithms are efficient in a large number of cases. The course finishes with an overview of source separation algorithms using sparsity prior.

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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 1 – Mar 2014 In this system, the popular fixed point algorithm with a new objective function called Modified Gaussian function to extract the independent components is used. The objective function is based on the approximation of negentropy that needs to be maximized. The statistical property of this function is demonstrated for its robustness in case of image mixtures. Further, we measure the quality of the separated images with Amari metric and assess the performance of separated images using ISNR and PSNR.

3. DOMAIN APPLICATION The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technology discipline, image processing is important to this and yes it is beneficial.

Another case where image processing techniques are used is face detection. It is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face detection can be regarded as a specific case of object-class detection; in objectclass detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.

Medical imaging refers to the techniques and processes used to create images of the human body for clinical purposes or medical science (including the study of normal anatomy and physiology). As a discipline and in its widest sense, it is part of biological imaging and incorporates radiology, radiological sciences, endoscopy, (medical) thermography and medical photography.

Microscope image processing is a broad term that covers the use of digital image processing techniques to process, analyze and present images obtained from a microscope. Such processing is now commonplace in a number of diverse fields such as medicine, biological research, cancer research, drug testing, metallurgy, etc. A number of manufacturers of microscopes now specifically design in features that allow the microscopes to interface to an image processing system. This is an area where the applications are yet to be developed but still I feel positive about it. And Using Image processing technique large scale poster is created with a small uploaded image through raster operation where an image is changed into postscript.

4.PROBLEM STATEMENT One reason this task is difficult is that the received signals are distorted versions of the originals. There are two types of distortions:  The first type arises from propagation through a medium, and is approximately linear but also history dependent. This type is termed reverberations.  The second type arises from background noise and sensor noise, which are assumed additive. The task is also difficult for another reason, which is lack of advance knowledge of the properties of the sources and of the distortions.

5. PROJECT OBJECTIVE The aim has been to take advantage of sparsifying dictionaries for this purpose. Unlike the existing sparsity-

ISSN: 2231-2803

based methods, we assumed no prior knowledge about the underlying sparsity domain of the sources. Instead, we have proposed to fuse the learning of adaptive sparsifying dictionaries for each individual source into the separation process.

6. PROJECT SCOPE Sparsity-based approaches for the BSS problem have received much attention recently. The term sparse refers to signals or images with small number of non zeros with respect to some representation bases. In sparse component analysis (SCA), the assumption is that the sources can be sparsely represented using a known common basis or dictionary.

7.MOTIVATION In this paper we consider the formulation of our algorithm from the point of view of minimizing the sparsity index on atoms. We seek the sparsity of the dictionary atoms alone rather than of the decomposition, and to the authors’ knowledge this perspective has not been considered elsewhere. Further, we propose a stopping rule that automatically selects only a subset of the atoms. This has the potential of making the algorithm even faster and to aid in denoising applications by using a subset of the atoms within the signal reconstruction.

8.EXSITING SYSTEM Blind source separation(BSS) is a method of extracting underlying source signals from a set of observed signal mixtures with little or no information about the nature of the source. The term ‘sparse’ refers to signals or images with small number of non- zeros respect to some representation bases. In Sparse Component Analysis(SCA), the sources are sparsely represented using a known dictionary. The aforementioned SPICA method considers the wavelet domain for this purpose[4]. Multichannel Morphological Component Analysis(MMCA) and Generalized Morphological Component Analysis(GMCA)[6] are proposed to address this problem.

The main assumption in MMCA is that each source can be sparsely represented in a specific known transform domain. In GMCA, each source is modeled as the linear combination of a number of morphological components where each component is sparse in a specific basis. MMCA and GMCA are extensions of a previously proposed method of morphological component analysis (MCA)[7] to the multichannel case. In MCA, the given signal/image is decomposed into different morphological components subject to sparsity of each componentork, the aim is to find an over complete dictionary that can sparsely represent given set of images or signals.

Utilizing Trained dictionaries into MCA has been shown to yield promising results. However, taking the advantages of Trained dictionaries in MMCA is still an open issue. One possible approach to use dictionary learning for MMCA is to learn a specific dictionary for each source from a set of exemplar images. This, however, is rarely the case, since in most BSS problems; such training samples are not available.

In this system, they adapt MMCA to those cases that the sparsifying dictionaries transforms are not available.

The existing algorithm is designed to adaptively learn the dictionaries from the mixed images within the source

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International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 1 – Mar 2014 separation process. In this, the multichannel case with more observations than sources is considered. They start by theoretically extending the denoising problem to BSS. Then, a practical algorithm is proposed for BSS without any prior knowledge about the sparse domains of the sources. The results indicate that adaptive dictionary learning, one for each source, enhances the separability of the sources.

8.1Disadvantages of Existing System  Speed of separation mechanism is less  De-noising method should be improved  Computationally expensive

: i D ji{



S

i}

, x j } λ || E j

The proposed system provides a popular fixed point algorithm with a new objective function called Modified Gaussian function to extract the independent components is used. The objective function is based on the approximation of negentropy that needs to be maximized. The statistical property of this function is demonstrated for its robustness in case of image mixtures. Further, we measure the quality of the separated images with Amari metric and assess the performance of separated images using ISNR and PSNR. Independent Component Analysis(ICA) is used for finding factors and components from multivariate statistical data and is one of the solution for the BSS problem. It looks for components for both statistical and non-Gaussian. ICA is widely used in statistical signal processing, medical image processing, economic analysis and telecommunication applications.

The popular ICA methods use a non-linear contrast function to blindly separate the signals. The contrast functions have proposed that the probability distributions are obtained by considering a maximum likelihood (ML) solution corresponding to some given distributions of the sources. This is adapted to temporally independent non-Gaussian sources and is based on the use of non-linear separating sources. There are two general purpose nonlinearities that have similar performances.

9.1 Advantages of Proposed System Used to solve the problem of Source Separation Can give optimal solution and simplicity Better quality of separation of the mixed images

i

: jX j i

||

 

F ix j :|

i

(2) Where Dj denotes the dictionary corresponding to the Jth source, i.e.,xj , and is the jth residual expressed as n

E

j

 a 

 Y 

: lX l : T

(3)

j

The minimization process for the jth level of hierarchy can be expressed as follows. The image patches are extracted from xj and then processed by K-SVD for learning Dj and sparse coefficients {si} , whereas other parameters are kept fixed.

Then, the gradient with respect to xj is calculated and set to zero

0   j (X j : a

: JT

- E j )a

: J 

  i (  ix j  D j S i ) i

  j X j    i i x j



 jE j a

: j-

(4)

D jS i

i

Finally, after some manipulations and simplifications in above equation, the estimation of the jth source is obtained as

Xj  (jEjaj   iDjSi ) /(jI   ii ) i

Proposed System

Existing System

Find solution for noiseless setting using MMCA

(5)

i Image Denoising using DCT and KSVD

Input image with noise

Offering sparse representations

9.2 Blind Source Separation

- a

j

 µ i || Si || 0   || D j S

i  1, l!

9.PROPOSED SYSTEM

  

{a

Dictionary initialization process

Modified FastICA image separation

High quality separated images

Dictionary updation process

The blind source separation follows a linear mixture

Separated image Output

model: Y=AX+V (1) Where, Y is the observation matrix X is the source matrix A is an (m x n) mixing matrix V is an (m x n) additive noise matrix BSS model is broken into rank-1 multiplications and the following minimization problem is defined:

ISSN: 2231-2803

Performance comparison

Figure 2:Block Diagram of Image Denoising using Modified FastICA

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|

2

International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 1 – Mar 2014 Initially the input image is given with adding some noise using Gaussian form, then later the noise is removed using DCT and K-SVD procedures for better results. The existing system defines in updating the dictionary process using MMCA. The BSS Algorithm is used to update the dictionary process with the input matrix. From the output image obtained, it is compared with the FastICA Algorithm which depends upon the image separation in the proposed system and the best quality image is produced.

10.EXPERIMENTAL RESULTS In this system two source images are given as the input images in form of matrix representation and Gaussian noise is added to mixed image and thus obtains the observation matrix.

11. CONCLUSION AND FUTURE WORK In this project an efficient and simple technique for sparsification of the natural observed mixtures followed by a blind separation of the original images has been proposed. As a future work we can implement a new method which can be based on an image encryption method by using the linear mixing model of blind source separation (BSS). It can simultaneously encrypt multiple images with the same size by mixing them with the same number of statistically independent key images, the size of which is equal to that of the images to be encrypted. The method based on sparse representation approach which can be used in blind source separation. In the existing system they developed the hierarchical method; a local dictionary is adaptively Trained for each source along with separation. To improve the quality of the image in proposed work, we are using the FastICA algorithm employing a modified Gaussian contrast function for the Blind Source Separation. In this system, the popular fixed point algorithm with a new objective function called Modified Gaussian function to extract the independent components is used. The objective function is based on the approximation of negentropy that needs to be maximized. The statistical property of this function is demonstrated for its robustness in case of image mixtures.

REFERENCES

Figure 2:Mixture of images with noise added

Figure 3:Then the dictionary is updated showing the iterations between producing the output image.

ISSN: 2231-2803

[1] A. Hyvärinen, J. Karhunen, and E. Oja, May 2001, Independent Component Analysis.New York: Wiley-Interscience.

[2] D. D. Lee and H. S. Seung ,Oct.1999, Learning the parts of objects By Nonnegative matrix factorization, Nature vol. 401, no.

6755, pp.788–791 [Online].

[3] A. Hyvarinen, May 1999, Fast and robust fixed-point algorithms for Independent component analysis, IEEE Trans. Neural Netw., vol.

10, no. 3, pp.626–634.

[4] A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, and Y. Zeevi, 2005,Sparse ICA for blind separation of transmitted and reflected Images, Int. J. Imaging Syst. Technol., vol. 15, no. 1, pp. 84–91.

[5] J. Bobin, Y. Moudden, J. Starck, and M. Elad, Jul.2006, “Morphological diversity and source separation,” IEEESignal Process. Lett., vol. 13, no. 7, pp. 409–412.

[6] J. Bobin, J. Starck, J. Fadili, and Y. Moudden, Nov. 2007 “Sparsity and morphological diversity in blind source separation,” IEEE Trans. Image Process., vol. 16, no. 11, pp. 2662– 2674.

[7] J. Bobin, J.-L. Starck, J. M. Fadili, Y. Moudden, and D. L. Donoho,Nov. 2007, Morphological component analysis: An adaptive thresholding strategy, IEEE Trans. Image Process., vol.16, no11, pp.

2675–2681.

[8] A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, and Y. Zeevi, 2005, Sparse ICA for blind separation of transmitted and reflected images, Int. J. Imaging Syst. Technol., vol. 15, no. 1, pp. 84–91.

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