Multiple Inpainting of low resolution images using ...ijarcet.org/wp-content/uploads/IJARCET-VOL-3-ISSUE-11-3641-3648.pdfImage inpainting techniques are in use over a long time for

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)

Volume 3 Issue 11, November 2014

ISSN: 2278 – 1323 All Rights Reserved © 2014 IJARCET 3641

Multiple Inpainting of low resolution images using

examplar and super resolution algorithm

A.Breezy Joy, C.Rekha

Abstract—Inpainting is the process of filling the missing regions

in an image. The main aim of this paper is to fill the missing areas

using examplar based inpainting and to recover the missing areas

and improve the quality of the image using super resolution

algorithm. The performance of the algorithm is evaluated using

PSNR, mean square error and Histogram error. The damaged

image is first downsampled. Then the image is inpainted a

number of times using examplar based approach. The inpainted

images are combined using loopy belief propagation. The image is

finally upsampled and the image quality is improved using a

super resolution algorithm.

Index Terms—examplar based inpainting, mean square error,

loopy belief propagation, running time.

1. INTRODUCTION

Inpainting is used for reconstructing lost or

deteriorated parts of images. Image inpainting techniques are

in use over a long time for various applications like scratch

removal, restoring the damaged, missing portions or removal

of objects from the images, etc. In photography and cinema,

inpainting is used for film restoration. To reverse the

deterioration, dust spots in film and see infrared cleaning. It is

also used for removing red-eye, eliminating the stamped date

from photographs and removing objects for creative effect.

This technique is be used to replace the lost blocks in the

transmission and coding of images such as in a streaming

video. It is also used to remove the logos in videos. If any

unwanted portion of the image is to be removed, then the

missing region can be filled using inpainting.

In a paper fragment based image completion,

adaptive neighborhood algorithm is used. Here the most

similar fragments from the neighborhood are selected and

filled in the missing regions. The visible part of the image

serve as a training set for the unknown parts. But in this

method, more time is spent for searching the best matching

fragments. Sometimes two fragments of same part may differ

under slight illumination changes. The next paper is

inpainting by patch propagation using patch sparsity. In this

method the missing patches are represented by sparse linear

combination of candidate patches. The patch with large

structure sparsity is used for inpainting. Here only the

structure of the patch is considered. Texture is not included.

Examplar based inpainting based on local geometry

uses K-nearest neighbor algorithm to find the structure tensor.

To find the best matching patch, template matching is

performed. This method fails to replicate texture in a coherent

manner. In texture synthesis method, the textural information

and structural information are propagated and the patch

priority is calculated. This method fails to handle curved

structures. If similar patches do not exist, it does not produce

reasonable output.

In a paper statistics of patch offsets for image

completion, the statistics of offsets which are sparsely

distributed are obtained. Some dominant offsets provide

reliable information for filling the image. A stack of shifted

images are combined by optimization and it is used to fill

missing region. Dominant offsets show how the patterns are

repeated and give details about the missing regions. The main

advantage is this method has better results and has fast image

completion. But this method fails when the desired offset does

not have dominant statistics. This problem can be solved only

partially by introducing manual offsets.

In non parametric sampling, a markov random field

model is assumed and the conditional distribution of each

pixel is calculated. A number of textures are synthesized from

a single texture. But some textures may slip into wrong part

and produce verbatim copies of the original. Only frontal

parallel textures are handled. Local linear embedding is used

to generate a high resolution image from a low resolution

image. Here more number of neighbours should be considered.

All these above methods have low signal to noise ratio and

image quality is low. These drawbacks are overcome by the

proposed method.

The proposed method uses examplar based

inpainting. Inpainting is performed on a coarse version of the

input image. For filling the missing regions a non parametric

patch sampling method is used. In this method the best

matching patches from the neighborhood are chosen and they

are filled in the missing region.To recover fine details on the

missing areas, super resolution algorithm is used which

converts the low resolution image back to high resolution

image.Template matching, non local means and non negative

matrix factorization. These three methods aimed at selecting

the best matching pixel which is to be replaced in the place of

inpainting region. The parameters like PSNR, MSE and

Histogram error are estimated.

2. PROPOSED METHOD

An image to be inpainted is taken. As low resolution

images are easier and efficient for inpainting, the image is first

downsampled. i.e., a high resolution image is converted into

low resolution image. Then the image is inpainted for a

number of times using examplar based inpainting. Then all

the inpainted images are combined using loopy belief

propagation. Then to improve the quality of the image, a super

resolution algorithm is applied on the image to improve image

quality and then convert the low resolution image back to high

resolution images. Thus the final inpainted image with high

resolution is obtained and the performance is evaluated.




Fig.2.1Block diagram for inpainting

3. EXAMPLAR BASED INPAINTING

The first step in image inpainting is to convert the

high resolution image into low resolution image into low

resolution image. For this converting process a Gaussian

pyramid decomposition is performed. A low pass filtering

operation is performed on the image pixels. Downsampling is

necessary because the low resolution images are easier to

inpaint. Low resolution images are contaminated by noise.

Local orientation singularities which affect the filling process

are strongly reduced if low resolution images are used.

After converting into low resolution images, mark the

target region to be filled using region of interest. The

remaining part of the image is the source region from which

the neighbouring patches are selected.

I

A Boolean matrix is constructed which stores a

binary value 1 for pixel to be inpainted and binary value 0 for

the other pixels. This matrix is called fill region. The next step

is to find the boundary of marked region by convolving

the fill region with laplacian filter. The next step is to find the

priority of the patch which is to be filled first. Texture

synthesis is performed which copies the samples from the

neighbourhood and replaced in the missing region.

3.1 FIND PATCH PRIORITY

The missing patch which has the highest priority is to

be found. Priority is the patch which has less unknown

regions. The patch priority is given by the product of data term

and confidence term.

P(p) = C(p) D(p)

Where P(p) is the patch priority

C(P) is the confidence term

D(p) is the data term

Data term can be either a tensor based or sparsity based data

terms. Tensor based priority term is given by

m

i

TIiIiJ1

Where J is the sum of structure tensors of each scalar image

channels. The scalar structure tensor undergoes smoothing

operation by

GJJ

Where G is the Gaussian distribution with standard deviation

. A similarity weight is measured between the known

patches and the unknown patch. For any selected patch, a

collection of neighbouring patches with highest similarities are

distributed in the same structure or texture. The confidence

values of structure for a patch are measured by the sparseness

of its non-zero similarities to the neighbouring patches. The

patch which is more sparsely distributed with non zero

similarities is placed on the fill front due to more structure

sparseness. For a patch located at the fill front , a

neighborhood window N (p) is set with centre p.

3.2 TEXTURE SYNTHESIS

Texture synthesis is the process of algorithmically

constructing a large digital image by taking advantage of the

structural content. Texture synthesis is used for filling the

missing parts. The patch which has the highest priority is filled

first. To fill a patch, the most similar patch from the remaining

part of the image is taken. A similarity metric is used to find

the best matching patch.

Texture synthesis is of two types. Pixel based texture

synthesis and patch based texture synthesis. In pixel based

method, a texture is synthesized in scan line order by finding

and copying pixels with most similar local neighborhood as

the synthetic texture. This method is very useful for image

completion.

IMAGE

N

Inpainting 1

Inpainting2

Inpainting 3

Belief

propagation

SR

algorithm

Enhanced

image

Inpainting n




In patch based texture synthesis, a new texture is

created by copying and stitching the textures at various offsets.

This method is more effective and faster than pixel based

texture synthesis. The chosen patch should have maximum

similarity between the known pixel values of the current patch

to be filled and the co-located pixel values.

Coherence measure is given by

)),((min)(jx

Sjx ppSSD

pp dcoh

dSSD- sum of square differences

Ψpx - current patch

Ψpj – neighbouring patch

Coherence measure is the degree of similarity

between the synthesized patch and the original patch. The

chosen neighbor should lie within min)1( d where

mind is the minimum distance between the current patch and

the closest neighbor.

If the selected patch does not satisfy this

condition, then the filling process is stopped and the priority of

the current patch is decreased. Again the patch with highest

priority is chosen. The unknown parts are pasted using the best

matching patch by direct sampling. Alpha blending is used to

combine the known part and the source patch. Alpha blending

is the process which displays a bitmap which has transparent

or semi transparent pixels. The image is made transparent and

the known part and unknown part are combined together.

Poisson fusion is the process used to hide the seams. The

unevenness caused in the image in the inpainting process is

corrected by using Poisson fusion. Patch size and filling order

must be considered. Patch size can be 55, 77, 99, 1111 etc. a parameter M is considered which is the number of

times inpainting is done.

3.3 LOOPY BELIEF PROPAGATION

LBP is a message passing algorithm. A node is used to pass a

message to the adjacent node only when it has received all the

messages, eliminating the message from the destination node

to itself. In the same way, all the inpainted images are

combined together using loopy belief propagation. A label is

assigned to each pixel of the unknown regions T of the image.

A major drawback of belief propagation is that it is slow when

the number of labels is high. So loopy belief propagation is

used to avoid this complexity.

To solve the problems like blur and spatial

consistency, there is a need to minimize the objective function.

The number of labels assigned must be equal to the number of

patches in the source region. In loopy belief propagation, the

number of labels is small. Label is the index of the inpainted

image from which the patches are extracted. A label is

assigned for each pixel so that the total energy E of the markov

random field is minimized.

Energy of MRF is given by

E(l)=∑ vd(lp) + ∑ Vs(ln , lm)

vd (lp) is the label cost

Vs (ln , lm) is the discontinuity cost

The cost increases when the similarity between the current

patch and collocated patches are less. Discontinuity cost is the

difference between labels. λ is the weighing factor and it is set

to 100.Using loopy belief propagation, minimization of energy

E is performed over the target region T and it corresponds to

maximum a posteriori (MAP) estimation problem. When λ is

0, there is no smoothness term. Some artifacts are visible. A

good trade-off is obtained by setting the value of λ to100.

4. SUPER RESOLUTION ALGORITHM

After combining all the inpainted images, a super

resolution algorithm is used to recover back the high

resolution image from the low resolution image. The

downsampled image is upsampled. It is also used to improve

the quality of the image. In low resolution image, the pixel

density within an image is small. Hence it offers fewer details.

In a high resolution image, the pixel density is larger. Hence it

offers more details. Super resolution is the process of

obtaining a high resolution image to low resolution image. To

increase the image resolution, the pixel size is reduced by

increasing the number of pixels per unit area. But the amount

of light available per pixel also decreases. The chip size can be

reduced but the increase of capacitance leads to storage

problem. SR image reconstruction is computationally effective

and cost is less.

5. ALGORITHM FOR INPAINTING

Read an image and find the size of the image. Using

median filter, filter the image which replace the pixel

value with the neighbourhood of its value.

Convert the RGB colour image into gray scale image.

Create a mask which is completely filled with 0 and

the area to be inpainted is marked as 1.

Create a label matrix which indicates the label for the

connected components and used to mark the part of

region to be inpainted.

To set the boundary (Ymax, ymin, Xmax, Xmin) in the

inpaint image, compute the values using the below

mentioned equations

1. Ymin=min(row)-1

2. Ymax=max(row)+1

3. Xmin=min(column)-1

4. Xmax=max(column)+1

Calculate the nearest matching pixel in column and

row,

Matching pixel row, A= (Ymax-Ymin)+1

Matchingpixelcolumn, B= (Xmax-Xmin) +1

Find the bottom, top, left and right area of the image,

1. Bottom Area Range (BAR) & Bottom Mean

If (Ymax+A) <= width of the image, then BAR=

(Ymin+A) to (Ymax+A) in x direction

=Xmin to Xmax in y direction.

Otherwise,

BAR = (Ymin+A) to width of image in x direction





∑1, 2….n pixels/ (number of pixels) =mean (BAR)

2. Top area range (TAR) & top mean

If (Ymin-A) >= 1, then

TAR= (Ymin-A) to (Ymax-A) in x direction

TAR=Xmin to Xmax in y direction.

Otherwise,

TAR=1 to (Ymax-A) in x direction


∑1, 2….n pixels/ (number of pixels)=mean(TAR)

3. Right area range (RAR) & right mean

If (Xmax+B) <= height of the image then,

RAR=Ymin to Ymax in x direction

=(Xmin+B) to (Xmax+B) in y direction.

Otherwise,

RAR=Ymin to Ymax in x direction

=(Xmin-B) to height of image in y direction.

∑1,2….n pixels/(number of pixels)=mean(RAR)

4. Left area range (LAR) & right mean

If (Xmin-A) >= 1, then,

LAR=Ymin to Ymax in x direction

=(Xmin-B) to (Xmax-B) in y direction.

Otherwise,

LAR=Ymin to Ymax in x direction

=1 to (Xmax-B) in y direction.

∑1,2….n pixels/(number of pixels)=mean(LAR)

In template matching method, the maximum of the

mean values of bottom, top, left and right area are

calculated. The area to be inpainted is replaced by the

maximum of the above four mean values.

In NLM method, if (bottom mean-top mean) is

greater than 20, assume the top to bottom value as 0,

otherwise 1.

If (left mean-right mean) is greater than 20, then the

left to right value is 0, otherwise 1.

If both top to bottom and left to right values are 1,

then the inpainting area is replaced by,

pixel=mean(bottom,top,right,left);

If top to bottom value is 1 and left to right value is 0

then the inpainting area is replaced by,

Pixel=mean (bottom_mean, top_mean)

If the top to bottom value is 0 and left to right value is

1, then the inpainting region is replaced by

Pixel=mean (right_mean,left mean)

If both top to bottom value and left to right are 0, and

(left mean-right mean) > (bottom mean-top mean),

then the inpainting region is replaced by,

Pixel=mean (bottom_mean, top_mean)

Otherwise the pixels are replaced by pixel=mean

(right_mean, left_mean)

In NMF method, consider a temporary value. If the

miss image is less than 10, the inpainting region is

replaced by

Missing pixel= max (temporary value)

Finally the image has to be enhanced. Median filter is

used to filter the images obtained from the three

methods and hence the image quality is increased.

The performance of inpainting is evaluated by

calculating PSNR mean square error and histogram

error.

6. RESULTS AND DISCUSSION

The final output consists of the input image, inpainted

image, TM image, NLM image and NMF image. First the

input image is displayed. Next the binary mask is displayed in

which the region to be inpainted is in white colour and the

remaining portion is displayed as black. The region to be

inpainted has the pixel value 1 and the remaining part has the

pixel value 0. Three methods of inpainting are performed and

their outputs are displayed. The image quality is improved and

the downsampled image is converted back to high resolution

image. The three methods include Template matching (TM),

non local means (NLM) method and non negative matrix

factorization (NMF) method. Output of each method is

displayed.

Then signal to noise ratio is measured for the three

images. But it does not have much variation. Next mean

square error is calculated. MSE also does not have

considerable changes among the three images. Finally

histogram error is calculated. Histogram error is less in TM

image than the other two images. Hence experimental results

show that among the three methods, TM image is better. Three

parameters, signal to noise ratio, mean square error and

Histogram error are estimated to evaluate the performance of

the three methods. Template matching is better than the other

two methods.

Four examples are considered. For baby image, leaf

image, nature image and blue image TM, NLM and NMF

methods of inpainting are performed. The table 6.1, shows that

the PSNR and MSE values are nearly equal and does not show

much changes for the three methods. To find which method is

better we have to go for another parameter. Histogram error is

calculated for TM, NLM and NMF methods. Histogram error

is less for TM image as compared to the other two methods.

Template matching is the process of selecting the best

matching pixel by taking the mean of the top, bottom, left and

right value. As the inpainting area is replaced by the mean of

the pixel, the inpainting process will be better. Hence TM

method is suitable for inpainting. The comparison of PSNR,

MSE and Histogram error is shown in the table below. The

output figures of each method with different examples are

shown below.




6.1 Comparison of PSNR, MSE and Histogram Error

Image name Method PSNR MSE Histogram Error

Baby

TM 49.3500

0.7552

2.7458e-005

NLM 49.2528

0.7723

3.1116e-005

NMF 49.3539

0.7545

2.8679e-005

Leaf TM 30.4311

26.8806

2.3023e-004

NLM 30.4109

26.1553

3.1217e-004

NMF 30.4445

26.6987

3.2135e-004

nature TM 38.0800

10.1177

8.7285e-005

NLM 38.0730

10.1340

8.9937e-005

NMF 38.0841

10.1082

9.2380e-005

blue TM 36.8829

13.3287

2.1635e-004

NLM 36.8840

13.3254

2.9234e-004

NMF 36.8841

13.3252

2.4539e-004

Table 6.1 Comparison of PSNR, MSE and Histogram Error

Original image damaged image inpainting region

TM image NLM image NMF image




Input image damaged image inpainting region


Input image Damaged image Inpainting area





Input image Damaged image Inpainting area

TM image NLM image NMFimage

7. CONCLUSION

Thus the inpainting was done for a series of images

using examplar based inpainting First the input image is

downsampled to make the inpainting easier. Then inpainting is

done for a number of times using three methods of inpainting.

They are template matching (TM), non local means (NLM)

and non negative matrix factorization (NMF). Finally a super

resolution algorithm is used to improve the quality of the

image. It undergoes a filtering operation to reduce the noise in

the image. Among the three methods, the PSNR and mean

square error does not show much variation. But histogram

error is low for TM image as compared to the other two

methods. Hence template matching is the best method suitable

for inpainting. Future work would be to extend this to other

super resolution methods and also to improve the image

quality after inpainting.

REFERENCES

[1] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester,

―Image inpainting,‖ in Proc. 27th Annu. Conf. Comput.

Graph. Interact. Tech., Jul. 2000, pp. 417–424.

[2] D. Tschumperlé and R. Deriche, ―Vector-valued image

regularization with PDEs: A common framework for

different applications,‖ IEEE Trans. Pattern Anal. Mach.

Intell., vol. 27, no. 4, pp. 506–517, Apr. 2005.

[3] T. Chan and J. Shen, ―Variational restoration of non-flat

image features: Models and algorithms,‖ SIAM J. Appl.

Math., vol. 61, no. 4, pp. 1338–1361, 2001.

[4] A. Criminisi, P. Pérez, and K. Toyama, ―Region filling

and object removal by examplar-based image inpainting,‖

IEEE Trans. Image Process., vol. 13, no. 9, pp. 1200–1212,

Sep. 2004.

[5] I. Drori, D. Cohen-Or, and H. Yeshurun, ―Fragment-

based image completion,‖ ACM Trans. Graph., vol. 22, no.

2003, pp. 303–312, 2003.

[6] P. Harrison, ―A non-hierarchical procedure for re-

synthesis of complex texture,‖ in Proc. Int. Conf. Central

Eur. Comput. Graph., Vis. Comput. Vis., 2001, pp. 1–8.

[7] C. Barnes, E. Shechtman, A. Finkelstein, and D. B.

Goldman, ―Patch- Match: A randomized correspondence

algorithm for structural image editing,‖ ACM Trans. Graph.,

vol. 28, no. 3, p. 24, Aug. 2009.

[8] A. A. Efros and T. K. Leung, ―Texture synthesis by non-

parametric sampling,‖ in Proc. 7th IEEE Comput. Vis.

Pattern Recognit., Sep. 1999, pp. 1033–1038.[9] O. Le

Meur, J. Gautier, and C. Guillemot, ―Examplar-based

inpainting based on local geometry,‖ in Proc. 18th IEEE Int.

Conf. Image Process., Sep. 2011, pp. 3401–3404.

[10] O. Le Meur and C. Guillemot, ―Super-resolution-based

inpainting,‖ in Proc. 12th Eur. Conf. Comput. Vis., 2012,

pp. 554–567.

[11] S. Dai, M. Han, W. Xu, Y. Wu, Y. Gong, and A.

Katsaggelos, ―SoftCuts: A soft edge smoothness prior for

color image super-resolution,‖ IEEE Trans. Image Process.,

vol. 18, no. 5, pp. 969–981, May 2009.

[12] W. T. Freeman, T. R. Jones, and E. C. Pasztor,

―Example-based superresolution,‖ IEEE Comput. Graph.

Appl., vol. 22, no. 2, pp. 56–65, Mar.–Apr. 2002.

[13] D. Glasner, S. Bagon, and M. Irani, ―Super-resolution

from a single image,‖ in Proc. IEEE Int. Conf. Comput. Vis.,

vol. 10. Oct. 2009, pp. 349–356.

[14] H. Chang, D.-Y. Yeung, and Y. Xiong, ―Super-

resolution through neighbour embedding,‖ in Proc. IEEE

Comput. Vis. Pattern Recognit., vol. 1. Jun.–Jul. 2004, pp.

275–282.




[15] Y. Wexler, E. Shechtman, and M. Irani, ―Space-time

video completion,‖ in Proc. IEEE Comput. Vis. Pattern

Recognit., Jun.–Jul. 2004, pp. I-120–I-127.

[16] Z. Xu and J. Sun, ―Image inpainting by patch

propagation using patch sparsity,‖ IEEE Trans. Image

Process., vol. 19, no. 5, pp. 1153–1165,

May 2010.

[17] S. Di Zenzo, ―A note on the gradient of a multi-image,‖

Comput. Vis., Graph., Image Process., vol. 33, no. 1, pp.

116–125, 1986.

[18] J. Weickert, ―Coherence-enhancing diffusion filtering,‖

Int. J. Comput. Vis., vol. 32, nos. 2–3, pp. 111–127, 1999.

[19] J. Kopf, W. Kienzle, S. Drucker, and S. B. Kang,

―Quality prediction for image completion,‖ ACM Trans.

Graph., vol. 31, no. 6, p. 131, 2012.

[20] A. Bugeau, M. Bertalmío, V. Caselles, and G. Sapiro,

―A comprehensive framework for image inpainting,‖ IEEE

Trans. Image Process., vol. 19, no. 10, pp. 2634–2644, Oct.

2010.

[21] D. D. Lee and H. S. Seung, ―Algorithms for non-

negative matrix factorization,‖ in Advances in Neural

Information Processing System. Cambridge, MA, USA:

MIT Press, 2000.

[22] A. Buades, B. Coll, and J. Morel, ―A non local

algorithm for image denoising,‖ in Proc. IEEE Comput. Vis.

Pattern Recognit., vol. 2. Jun. 2005, pp. 60–65.

[23] P. Pérez, M. Gangnet, and A. Blake, ―Poisson image

editing,‖ in Proc. SIGGGRAPH, 2003, pp. 313–318.

[24] N. Komodakis and G. Tziritas, ―Image completion

using efficient belief propagation via priority scheduling and

dynamic pruning,‖ IEEE Trans. Image Process., vol. 16, no.

11, pp. 2649–2661, Nov. 2007.

[25] Y. Boykov, O. Veksler, and R. Zabih, ―Efficient

approximate energy minimization via graph cuts,‖ IEEE

Trans. Pattern Anal. Mach. Intell., vol. 20, no. 12, pp.

1222–1239, Nov. 2001.

[26] Y. Boykov and V. Kolmogorov, ―An experimental

comparison of mincut/ max-flow algorithms for energy

minimization in vision,‖ IEEE Trans.

Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1124–1137,

Sep. 2004.

[27] J. Yedidia, W. Freeman, and Y. Weiss, ―Constructing

free energy approximations and generalized belief

propagation algorithms,‖ IEEE Trans. Inf. Theory, vol. 51,

no. 7, pp. 2282–2312, Jul. 2005.

[28] A. Blake and A. Zisserman, Visual Reconstruction.

Cambridge, MA, USA: MIT Press, 1987.

[29] Y. Pritch, E. Kav-Venaki, and S. Peleg, ―Shift-map

image editing,‖ in Proc. Int. Conf. Comput. Vis., Sep. 2009,

pp. 151–158.

[30] K. He and J. Sun, ―Statistics of patch offsets for image

completion,‖ in Proc. 12th Eur. Conf. Comput. Vis., 2012,

pp. 16–29.

[31] J. Hays and A. A. Efros, ―Scene completion using

millions of photographs,‖ ACM Trans. Graph., vol. 26, no.

3, p. 4, 2007.

[32] O. Whyte, J. Sivic, and A. Zisserman, ―Get out of my

picture! Internetbased inpainting,‖ in Proc. 20th Brit. Mach.

Vis. Conf., 2009, pp. 1–11.

Breezy Joy A received the B.E degree in

Electronics and Communication

Engineering from Anna University,

Chennai, 2013.She is currently doing his

master of engineering in Communication

systems in Pet engineering college,

vallioor. Her areas of interests include

image processing, communication and

wireless systems.

Rekha C received the B.E degree in

Electronics and Communication Engineering

from Anna University, Chennai, 2005 and

M.E degree from Anna University,

Tirunelveli, 2011. She is currently working

as an Assistant Professor in Department of

Electronics and communication in Pet

engineering College, Vallioor. Her research

areas include antennas theory, microwave

systems,transmission lines and wave guides.

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Multiple Inpainting of low resolution images using ...ijarcet.org/wp-content/uploads/IJARCET-VOL-3-ISSUE-11-3641-3648.pdfImage inpainting techniques are in use over a long time for