Parallel Processing for Digital Image Enhancement

PARALLEL PROCESSING
FOR DIGITAL IMAGE
ENHANCEMENT
Nora Youssef Fahmy
B.Sc. in Computer and Information Sciences,
Teaching assistant at Computer Science Department
Faculty of Computer and Information Sciences
Ain Shams University
Supervised By
Prof. Dr. El-Sayed M. El-Horbaty
Dr. Abeer M. Mahmoud
Cairo 2015

AGENDA
 Introduction
 Problem Definition
 Objectives
 Related Work
 Methodologies
 Conclusions & Future Work
 Publications
 References

INTRODUCTION
Image
Processing
Grayscale
Image
Medical Image
Enhancement &
Restoration
Enhancement
Domains
Noises
Quality Metrics

GRAYSCALE IMAGE
 Monochrome / One color image
 No color info
 Represent the brightness of the image
 8 bits/pixel data  256 different brightness
level

MEDICAL IMAGE
 Used to create images of the human body
 Seeks to
 Reveal internal structures hidden by the skin and bones
 Diagnose and treat disease
PET CT MRI

DEGRADATION MODEL
 Noises
 Domains
 Filters
𝒈(𝒙, 𝒚)
𝒇^
(𝒙, 𝒚)
Noise
𝜼(𝒙, 𝒚)
+
DEGRADATION RESTORATION
𝒇(𝒙,𝒚)
Degradation
function
H
Restoration
Filter(s)

GAUSSIAN NOISE
 Additive
 𝑝 𝑧 =
1
2𝜋 𝛿
𝑒−(𝑧−𝑧`) 2𝛿2
 Due to:
 Poor illumination
 High temperature

SALT & PEPPER NOISE
 Additive
 𝑝 𝑧 =
𝑃𝑎 𝑓𝑜𝑟 𝑧 = 𝑎
𝑃𝑏 𝑓𝑜𝑟 𝑧 = 𝑏
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
 Due To:
 Quick transients
 Data transmission errors

IMAGE ENHANCEMENT &
RESTORATION
 Attempts to recover an image that has been distorted
by using a priori knowledge of the degradation
process.
𝒈(𝒙, 𝒚)
𝒇^
(𝒙, 𝒚)
Noise
𝜼(𝒙, 𝒚)
+
DEGRADATION RESTORATION
𝒇(𝒙,𝒚)
Degradation
function
H
Restoration
Filter(s)

ENHANCEMENT DOMAINS
 Spatial Domain
 Transform Domain (Frequency Domain)

SPATIAL DOMAIN
 𝑔(𝑥, 𝑦) = 𝑇[𝑓(𝑥, 𝑦)]
 Direct manipulation of pixels in an
image
 Covers the neighborhood operations
(Convolution)
 Filters in this domain are time
consuming

SPATIAL FILTERING
Filters
Linear
Arithmetic Geometric
Harmonic
Contra-
Harmonic
Non-Linear
Order
Statistics
Min Max
Midpoint Median
Alpha
trimmed
Adaptive
Local Noise
Reduction
Adaptive
Median

TRANSFORM DOMAIN
(FREQUENCY DOMAIN)
 𝐹(𝑢, 𝑣) = 𝑅[𝑓(𝑥, 𝑦)]
 𝑔(𝑥, 𝑦) = 𝑅−1 𝑇[𝐹(𝑢, 𝑣)]
 A representation of an image as a sum of complex
exponentials of varying magnitudes, frequencies, and
phases

FREQUENCY FILTERING
 Filters in spatial domain have their corresponding form in
frequency domain
 Convolution in the spatial domain corresponds to multiplication in
the frequency domain, and vice versa
 Wiener filter
 Gaussian removal
 De-blurring

QUALITY METRICS
 Used for dynamically monitor and adjust image quality
 MSE =
1
𝑀𝑁 𝑖
𝑀
𝑗
𝑁
[𝑔 𝑖, 𝑗 − 𝑓(𝑖, 𝑗)]2
 𝑆𝑁𝑅 = 10 𝑙𝑜𝑔10 (
𝑖
𝑀
𝑗
𝑁
𝑓(𝑖,𝑗)
𝑖
𝑀
𝑗
𝑁[𝑔 𝑖,𝑗 −𝑓(𝑖,𝑗)]2)
 𝑃𝑆𝑁𝑅 = 10 𝑙𝑜𝑔10 (
2552
𝑀𝑆𝐸
)

INTRODUCTION
Parallel
Processing
Algorithm
Features
Tools
Decomposition
Classifications
Metrics

PARALLEL ALGORITHM FEATURES
 Granularity
 Coarse-grained
 Fine-grained
 Type of parallel processing
 Explicit
 Implicit
 Synchronization
 Latency
 Scalability

DECOMPOSITION CLASSIFICATIONS
 Data Decomposition – (Fine Grained)
Data
D1 D2 D3 D4 D5 … Dn

DECOMPOSITION CLASSIFICATIONS
 Task (Functional) Decomposition – (Coarse Grained)
Code Block (Group of Functions)
Fn1
Fn2
Fn3
Fn4
Fn5
… Fnn

TOOLS
MATALAB
Client
MATLAB
Workers
parfor

METRICS
 Speed Up  𝑆 𝑃 =
𝑇𝑠
𝑇𝑝
 Efficiency  𝐸𝐹𝐹 =
𝑆 𝑃
𝑃

PROBLEM DEFINITION
 Images are corrupted by multi-noise type (i.e. Gaussian, salt &
pepper) at the same time.
 There is an open demand for multi-noise removal filters.

PROBLEM DEFINITION
 Medical images have very large size
 Processing these forms take so much time to process
sequentially
 We have to parallelize the traditional sequential
algorithms for the sake of time performance
improvement.

OBJECTIVES
1. Study the literature work and implement Gaussian de-noising
experiment as a case study.

OBJECTIVES
2. Design & Develop sequential hybrid de- noising filter for multi-
noise removal and compare between it and the simple filters in terms
of peak signal to noise ratio (PSNR).

OBJECTIVES
3. Design & Develop a parallel hybrid de-noising filter then
compare it relative to the sequential hybrid above in terms of
time.

RELATED WORK
 Single Noise Removal
 Multi-Noise Removal
 Parallel Image Processing

Year Author Contribution Noise to Remove Input Quality Criteria
2014 Monika P. and
Sukhdev S
Comparative study on
images de-noising
techniques
Salt & pepper Lena Image MSE
PSNR
2014 Rupinder Kaur,
Prabhpreet Kaur
Novel technique and
comparison
Speckle Ultrasound
image
SNR
PSNR
CoC
EPI
2014 P.Deepa and
M.Suganthi
Overview on image de-
noising techniques
Gaussian Lung CT image MSE
PSNR
2014 Sezal Khera and
Sheenam
Malhotra
Comparative study for
de-nosing techniques
Gaussian, Slat &
Pepper, Poison
and Speckle
MRI, X-Ray, CT PSNR

Year Authors Distortion Hybrid Input
Quality
Criteria
2014 Seema and
Meenakshi
G.
Speckle + Gaussian Median Filter based on DWT
via soft thresholding +
Mean absolute difference
Standard gray scale
images
PSNR
2013 Ankita D.
and et.al
Camera & object
motion blurs +
Gaussian +
Salt & pepper
Wiener + Median Gradient images PSNR
MSE
2013 Versha R.
and
Priyanka K.
Gaussian +
impulsive +
Speckle +
Possion
Curvelet transform +
Unsharp Mask filter +
Median filter
One gray & One
colored images
PSNR
2012 J U. and G
R.
Gaussian + Impulsive Haar wavelet filter +
soft thresholding technique +
center weighted median
10 DICOM images PSNR
MAE
UQI
ET

Year Author(s) Contribution Techniques
2015 Sharanjit Singh and
et.al
Implementation 1. Functions Extractions of Images
2. Clustering of 1
3. Histogram generation of 2
2014 Er.Paramjeet kaur
and Er.Nishi
Analysis CUDA, DicrectCompute, OpenCL
2013 Sanjay Saxena and
et.al
Implementation Algorithms
1. Image Segmentation
2. Image de-noising
3. Histogram Equalization

GAUSSIAN DE-NOISING EXPERIMENT

GAUSSIAN DE-NOISING ALGORITHMS
Filters
Linear
Arithmetic Geometric
Harmonic
Contra-
Harmonic
Non-Linear
Order
Statistics
Min Max
Midpoint Median
Alpha
trimmed
Adaptive
Local Noise
Reduction
Adaptive
Median

ENVIRONMENT SETTINGS
 Input:
 Mode: Gray scale – no color info
 Dimensions: average 512x512
 Distortion
 Gaussian Noise
 Mean = 0 &Variance = 1000
 Filter Size
 3x3, 5x5, 7X7 and 9x9
 Machine Specs:
 Processor: Intel core i5
 RAM: 4 GB
 OS: Windows 7 home edition, 64 bit
 Language: C#
 Tool:

SAMPLE RUN
Geometric Harmonic Midpoint
Alpha
Trimmed
Local Noise
Reduction
GroundTruth
Of 3X3

METHODOLOGIES
1. Serial Hybrid Algorithm
2. Parallel Hybrid Algorithm

SERIALHYBRIDALGORITHM
Fourier
Transform
Wiener
Inverse
Fourier
Transform
Adaptive
Median
Add Gaussian
Add Salt &
Pepper
Circular Blur
Image
Noisy
Image
Power
Spectrum
Restored
Image
Spatial
Domain
Fourier
Transform
Domain

PSEUDO-CODE
original = read image from file
original = convert to double
values
noisyBuf = degradImage(original)
procedure hybridFilter (noisyBuf)
adapOnly = apply adaptive
median filter
adapMedian(noisyBuf, 7 pix)
hybrid = apply wiener filter
wiener(adapOnly, original)
hybrid = apply
contrast(hybrid)
end procedure
procedure degradeImage(original)
I1 = Add circular blur to original
I2 = Add Gaussian noise to I1
I3 = Add Salt & Pepper noise to I3
return I3 as Noisy buffer
end procedure
1

PSEUDO-CODE
values
median filter
hybrid = apply
contrast(hybrid)
end procedure
procedure adapMedian(g, Smax)
f = g; %initial setup
alreadyProcessed = false(size(g));
for all window size k = start from 3 to Smax do:
get Min, max and median Values of window and
store it in zmin, zmax and zmed;
determine if we will use level b processing in
processByLvlB;
zB = (g > zmin) & (zmax > g);
outputZxy = processByLvlB & zB;
outputZmed = processByLvlB & ~zB;
f(outputZxy) = g(outputZxy);
f(outputZmed) = zmed(outputZmed);
alreadyProcessed = alreadyProcessed or
processUsingLevelB;
if all alreadyProcessed array has been done
break the loop
end if
end for
fill the remaining values in alreadyProcessed if
exist by the zmed
end procedure
1
2

PSEUDO-CODE
values
median filter
hybrid = apply
contrast(hybrid)
end procedure
procedure wiener(adapOnly, original)
F = create power spectrum of the adapOnly
sigma_u = calculate noise sigma in F
output = wiener deconvolution for F given the
original image and sigma_u
end procedure
1
2
3

PSEUDO-CODE
values
median filter
hybrid = apply
contrast(hybrid)
end procedure
procedure contrast(image)
stretch image’s pixels values to fit in range 0 -
255
end procedure
1
2
3
4

 Input:
 Mode: Gray scale – no color info
 Dimensions:
 512 X 512 (0.5 MB)
 128 X 128 (17 KB)
 Resolution: 300px
 Distortion
 Gaussian
 Salt & Pepper
 Circular Blur
 Machine Specs:
 RAM: 4 GB
 OS: Windows 7 home edition, 64 bit
 Tool:

RESULTS
 Average PSNR for Adaptive, Wiener and Serial Hybrid
Image a b c d e f g h I J k l AVG
Adaptive 10.04 14.15 14.2 12.06 13.3 13.7 13.43 9.5 13.29 17.12 6.88 12.45 12.6
Wiener 11.63 23.37 18.63 14.37 16.46 8.79 12.39 11.6 15.06 22.53 11.92 16.73 15.3
Hybrid 13.36 29.7 26.7 22.75 19.32 18.34 17.66 18.84 18.97 26.04 24.69 20.64 19.8

0
5
10
15
20
25
30
35
a b c d e f g h i j k l
PSNR
IMAGE
PSNR chart for full size images for proposed hybrid approach, adaptive
& wiener
Hybrid
Wiener
Adaptive

0
5
10
15
20
25
30
35
PSNR
IMAGE
Thumbnails VS. full Sizes of PSNR values
Thumbnails
Full Sizes

0
10
20
30
40
50
60
TIME(MINS)
IMAGE
Thumbnails VS. full Sizes time plot in minutes
Thumbnails
Full Size

PARALLEL HYBRID
ALGORITHM
 Adaptive Median takes
too much time
 Spatial domain
 Window size increases
Fourier
Transform
Wiener
Inverse
Fourier
Transform
Adaptive
Median
Add Gaussian
Add Salt & Pepper
Circular Blur
Image
Noisy
Image
Power
Spectrum
Restored
Image
Spatial
Domain
Fourier
Transform
Domain

PARALLELHYBRIDALGORITHM
Fourier
Transform
Wiener
Inverse
Fourier
Transform
Adaptive
Median
Add Gaussian
Add Salt &
Pepper
Circular Blur
Image
Noisy
Image
Power
Spectru
m
Restored
Image
Spatial
Domain
Fourier
Transform
Domain

PSEUDO-CODE
values
procedure parallelHybridFilter
(noisyBuf)
median filter
parAdapMedian(noisyBuf, 11
pix, workersNo)
hybrid = apply
contrast(hybrid)
end procedure
procedure degradeImage(original)
I1 = Add circular blur to original
I2 = Add Gaussian noise to I1
I3 = Add Salt & Pepper noise to I3
return I3 as Noisy buffer
end procedure
1

PSEUDO-CODE
values
(noisyBuf)
median filter
pix, workersNo)
hybrid = apply
contrast(hybrid)
end procedure
procedure parAdapMedian(g, Smax, workersNo)
for each worker in workersNo do in Parallel
apply adaptive median filter convolution
procedure on g
end for
end procedure
1
2

PSEUDO-CODE
values
(noisyBuf)
median filter
pix, workersNo)
hybrid = apply
contrast(hybrid)
end procedure
procedure wiener(adapOnly, original)
F = create power spectrum of the adapOnly
sigma_u = calculate noise sigma in F
output = wiener deconvolution for F given the
original image and sigma_u
end procedure
1
2
3

PSEUDO-CODE
values
(noisyBuf)
median filter
pix, workersNo)
hybrid = apply
contrast(hybrid)
end procedure
procedure contrast(image)
stretch image’s pixels values to fit in range 0 -
255
end procedure
1
2
3
4

 Input:
 Mode: Gray scale – no color info - 500px
 Dimensions:
 1900 x 2368 (277 KB)
 3800 x 4736 (715 KB)
 15200 x 18944 (2.5 MB)
 Distortion:
 Gaussian
 Salt & Pepper
 Circular Blur
 Machine Specs:
 RAM: 6 GB
 OS: Windows 7 enterprise edition, 64 bit
 Tool

RESULTS
 Time in seconds for the 3 image sizes each of which
divided into 2,4 an 8 partitions
 Workers rang 2 - 12
Image Dimensions Serial Workers 2 4 6 8 10 12
1900 X 2368 79.5
2-Partitions 10.41 10.87 10.89 10.37 10.96 11.11
4- Partitions 12.1 11.99 12.22 12.29 12.49 12.23
8- Partitions 16.22 15.84 15.87 16.62 15.64 16.2
3800 X 4736 272.42
2- Partitions 44.67 41.59 36.14 35.12 42.86 49.28
4- Partitions 41.37 40.76 40.62 41.06 41.35 42.18
8- Partitions 54.88 54.65 56.63 56.85 54.69 55.21
7600 X 9472 1742.5
2- Partitions 640.34 863.87 856.24 652.75 914.24 690.8
4- Partitions 768 470.08 480.03 532.75 490.92 703.38
8- Partitions 847.9 538.55 769.91 505.67 656.4 611.59

0
100
200
300
400
500
600
700
800
2 4 6 8 10 12
TIME(SECONDS)
WORKER
Average time consumed in seconds for serial and parallel 2,4 and 8
partition input
Serial
2-Partition
4-Partition
8-Partition

0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2 4 6 8 10 12
SPEEDUP
WORKER
Average speed up for 2, 4 and 8 partition input
SP -2
Sp - 4
sp - 8

0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
2 4 6 8 10 12
EFFICIENCY
WORKER
Efficiency for 2, 4 and 8 partition input
EFF -2
EFF - 4
EFF - 8

CONCLUSIONS
 Simple Gaussian filters comparison showed for the average run 3, 5,
7 and 9 pix that
 Harmonic filter gave the max. PSNR
 Geometric filter gave the min. Time

CONCLUSIONS
 Presented serial hybrid filter gave PSNR enhancement
 33.33% VS. Wiener
 55.55% VS. Adaptive median

CONCLUSIONS
 Parallel hybrid filter gave a speed up
 3x for 2-partition
 4x for 4-partition
 3.5x for 8-partition

FUTURE WORK
 Apply the proposed methods medical imaging modalities like PET
(Colored).
 Try to corrupt the image with higher noise probabilities.
 Change the noise type to but take care that it suits the selected
adaptive median and wiener filters.
IMAGE PROCESSING

FUTURE WORK
 Try Different parallel model
 GPU
 CPU + GPU (Hybrid Parallel).
 Compare time performance with the existing methods.
 Integrate with cloud computing.
PARALLEL PROCESSING

PUBLICATIONS
 Nora Youssef, Abeer M.Mahmoud and EL-
Sayed M.EL-Horbaty “Gaussian De-
Noising Techniques in Spatial Domain for
Gray Scale Medical Images”, Int. J. of
Information Technologies and Knowledge
(ITK), Vol. 8, No.3, pp.90-100, Bulgaria,
Jun. 2014.

PUBLICATIONS
 Nora Youssef, Abeer M.Mahmoud and
EL-Sayed M.EL-Horbaty “A Hybrid De-
Noising Technique for Multi-noise
Removal on Gray Scale Medical
Images”, Int. J. of Tomography and
Simulation (IJTS) IF 0.75, Vol. 28, No.2, pp
106-116, India, Mar. 2015.

PUBLICATIONS
 Nora Youssef, Abeer
M.Mahmoud and EL-
Sayed M.EL-Horbaty
“Gaussian De-Noising
Techniques” Lambert
Academic Publishing,
Germany, Apr. 2015.

PUBLICATIONS
 Nora Youssef, Abeer M.Mahmoud and
EL-Sayed M.EL-Horbaty “A Parallel Hybrid
Technique for Multi-Noise Removal from
Grayscale Medical Images” Int. J Real
Time Image Processing, Springer, Berlin,
May 2015 (Submitted).

REFERENCES
 Ravi M. Rai and Urooz J. “Analysis Techniques For Eliminating Noise In Medical
Images Using Bivariate Shrinkage Method” Int. J. of Advanced Research in
Computer Engineering & Technology vol. 2, no. 10, pp. 2737 to 2740, 2013.
 Ankita D. and Archana S. “An Advanced Filter for Image Enhancement and
Restoration” J. Open Journal of Advanced Engineering Techniques OJAET vol.1,
no. 1, pp. 7-10, 2013.
 Salem Al-amri, N. V. Kalyankar and S. D. Khamitkar “A Comparative Study of
Removal Noise from Remote Sensing Image” Int. J. of Computer Science Issues,
vol. 7, no. 1, pp. 32 - 35, 2010.
 Sanjay S., Neeraj S. and Shiru S. "Image Processing Tasks using Parallel
Computing in Multi core Architecture and its Applications in Medical Imaging"
Int. J. of Advanced Research in Computer and Communication Engineering
vol. 2, no. 4, pp. 1896 to 1899, 2013

REFERENCES
 Rajeshwari S., Sharmila T. Sree “Efficient quality analysis of MRI image using
preprocessing techniques,” Proceedings of 2013 IEEE Conference on Information
and Communication Technologies (ICT), pp.: 391-396, 2013.
 Rafael C. Gonzalez and Richard E. Woods “Digital Image Processing” Person
Education,3rd Edition, 2008.
 Nikola R. and Milan T. “Improved Adaptive Median Filter for Denoising Ultrasound
Images” Conf. Advances in Computer Science pp. 196 - 174, 2012
 P.Deepa and M.Suganthi “Performance Evaluation of Various Denoising Filters for
Medical Image”, IJCSIT, Vol. 5, No.3, pp. 4205-4209 , 2014

REFERENCES
 B.Mohd. Jabarullah, Sandeep Saxena and Dr.C. Nelson Kennedy Badu“Survey
on Noise Removal in Digital Images” Vo. 6, No. 4, pp.45 -51 2012
 Gajanand G.“Algorithm for Image Processing Using Improved Median Filter and
Comparison of Mean, Median and Improved Median Filter” IJSCE, vol. 1, no.5
pp. 304 – 311, 2011.
 K.Selvanayaki, Dr. M. Karnan “CAD System for Automatic Detection of Brain
Tumor through Magnetic Resonance Image-A Review” Int. J. of Engineering
Science and Technology vol. 2, no.10, pp. 5890-5901, 2010.
 Monika P. and Sukhdev S. “Comparative Analysis of Image Denoising
Techniques” Int. J. of Computer Science & Engineering Technology (IJCSET) vol.
5 no. 02 pp. 160 - 167, 2014

APPENDICES
 Serial Code
 Parallel Code
 Data Sets
 Publications Materials

original = imread(imPath);
original = im2double(original);
noisyBuf = degradImage(original);
procedure hybrid (noisyBuf)
adapOnly = apply adaptive median filter (noisyBuf, 7);
hybrid = apply wiener filter (adapOnly, original);
hybrid = apply contrast(hybrid);
end procedure
procedure adapMedian(g, Smax)
for all window size k = start from 3 to Smax do:
zmin = get Min Value of window;
zmax = get Max Value of window;
zmed = get Median Value of window;
processByLvlB determine if we will use level b
processing;
f(outputZxy) = g(outputZxy);
f(outputZmed) = zmed(outputZmed);
alreadyProcessed = alreadyProcessed or
processUsingLevelB;
if all alreadyProcessed array has been done
break the loop
end if
end for
fill the remaining values in alreadyProcessed if exist by
the zmed
end procedure
SERIAL CODE

PARALLEL CODE
original = imread(imPath);
original = im2double(original);
noisyBuf = degradImage(original);
procedure parallelHybrid (noisyBuf)
adapOnly = parallelAdapMedian(noisyBuf, 11, partition_i);
partition_i belongs to {2, 4, 8};
hybrid = apply Wiener function(adapOnly, original);
hybrid = apply contrast(hybrid);
end procedure
procedure parallelAdapMedian(g, Smax, parts)
[M, N] = size(g);
splittedImg = imgSpliter( f, M, N, parts );
for subimg = 1 into parts, numberOfWorkers_i do in parallel
numberOfWorkers_i belongs to {2, 4, 6 , 8, 10, 12}
myTemp = splittedImg(subimg);
t = alreadyProcessed;
for k = 3 to Smax do
zmin = get Min. value in subimg
zmax = get Max. value in subimg
zmed = get Median value in subimg
processByLvlB = determine if we will use level b
processing
myTemp(outputZxy) = g(outputZxy);
myTemp(outputZmed) = zmed(outputZmed);
end for
splittedImg(subimg) = myTemp;
end parfor
end procedure

DATA SETS
 idoimagaing
 http://idoimaging.com/wiki/tiki-
index.php?page=Sample+Data
 3DISC
 http://www.3discimaging.com/our-products/medical-
solutions/firecr-medicalreaders/image-quality/
 Computed Tomography Emphysema Database
 http://image.diku.dk/emphysema_database/
 Some of images are available on Google Images.

GAUSSIAN DE-NOSING
TECHNIQUES IN SPATIAL
DOMAIN FOR GRAY SCALE
MEDICAL IMAGES
Published
International Journal
"Information Technologies &
Knowledge"
Volume 8
 Number 3
ISSN 1313-0455
Bulgaria
2014
Click here…

A HYBRID DE-NOISING TECHNIQUE
FOR MULTI-NOISE REMOVAL ON
GRAY SCALE MEDICAL IMAGES
Published
International Journal of
Tomography and Simulation
Volume, 28
 Number, 2
ISSN, 2319-3336
IF 0.75
India
2015
Click Here…

GAUSSIAN DE-NOSING
TECHNIQUES
Published
Book Chapter
ISBN: 978-3-659-49570-0
Lambert Academic Publishing
(LAP)
Germany
2015
Click Here…

A PARALLEL HYBRID TECHNIQUE
FOR MULTI-NOISE REMOVAL
FROM GRAYSCALE MEDICAL
IMAGES
Submitted
Int. J. Real Time Image Processing
Springer
IF: 1.1
Germany
2015
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Parallel Processing for Digital Image Enhancement

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