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IMPROVED SWITCHING MEDIAN FILTER FOR IMPULSE NOISE REMOVAL
Denis K. Kuykin, Vladimir V. Khryashchev, Ilya V. Apalkov
e-mail: connect@piclab.ru
P.G. Demidov Yaroslavl State UniversityDigital Circuits and Signals Laboratory
2
Agenda
1. Introduction
2.Proposed Algorithms
3.Research Results for Salt-and-Pepper Noise
4.Research Results for Random Valued Impulse Noise
5.Conclusion
3
Classic Median Filtering
20% salt-and-pepper noise median filter (5×5 mask size)
4
Introduction
The problem is
• to develop modified algorithms based on rank-order statistics and switching
schema for restoration of digital images corrupted with salt-and-pepper and
random valued impulse noises
• to compare proposed algorithms with known ones
Conditions to the algorithms:
• effectiveness with wide range of noise ratio
• reasonable computational complexity
• efficient impulse noise removal
• preservation of object edges
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Peak Signal to Noise Ratio
N
iiixN
PSNR
1
2
2
)(1
255lg10
ix − restored image pixel value
i − original image pixel value
N − image pixels number
6
Image Quality Criteria Correlation withHuman Estimation
Distortion type
Image quality metrics
PSNR UIQ SSIM PSNR-M UIQ-M VIF
Gaussian blurring 0,7428 0,7135 0,6514 0,8563 0,8592 0,9077
JPEG 0,4882 0,7762 0,6223 0,8129 0,8608 0,8784
JPEG2000 0,8428 0.8170 0,8433 0,9063 0,8965 0,9467
Impulse noise 0,944 0,8824 0,8959 0,9445 0,8736 0,9624
Additive Gaussian noise
0,9478 0,9184 0,9397 0,9497 0,9239 0,9552
Averaged value 0,7931 0,8226 0,7905 0,8939 0,8828 0,9301
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Switching Image Restoration Schema
corrupted image
restored image
preliminary detection step
filtration procedure
8
Proposed Algorithms
1. Modified Progressive Switching Median Filter for Salt-and-Pepper Noise Removal
2. Modified Progressive Switching Median Filter for Random Valued Impulse Noise Removal
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Salt-and-Pepper Impulse Noise Model
)(1
255
0
pni
p
n
i
ppyprobabilitwith
pyprobabilitwith
pyprobabilitwith
x
ix - corrupted image pixel values
i - original image pixel values
np - negative impulses density
pp - positive impulses density
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Salt-and-Pepper Impulse Noise Model(known restoration algorithms)
CLASSICAL MEDIAN FILTER
• preserves object edges• misses a lot of noisy pixels• corrupts “good” image pixels
PROGRESSIVE SWITHING MEDIAN FILTER (PSM)(Wang Z., Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Trans. on Circuits systems – II (1) (1999). V. 46, pp. 78-80.)
• preserves object edges• is unable to remove blotches from highly corrupted images
ADAPTIVE MEDIAN FILTER (AMF)
• removes blotches from highly corrupted images• often corrupts object edges• high computational complexity
11
Proposed MPSM Algorithmfor Salt-and-Pepper Noise Removal
(noise detector)
}{ ix
}{ if
DWW
}|min{min Wiji jx
}|max{max Wiji jx
1
0
maxmin
i
i
iii
felse
fthen
xif }{ f
noise detection
result
}{ ix − input corrupted image }{ if − binary matrix
12
Proposed MPSM Algorithmfor Salt-and-Pepper Noise Removal
(filtration procedure)
}{}{ 0 xx }{}{ 0 ff FWW
}|{ 1 Wi
nj
ni jxmedmed
Wij
njfM 1
11
21
,
0,
1
ni
ni
ni
ni
ni
ni
ni
ni
ffxxelse
fmedxthen
WMandfif }{ f
restored image
}{ 1nix
}{ 1nif
i
nifK
0K
0K
13
Comparative Analysis of Efficiency(classic algorithms)
14
Comparative Analysis of Efficiency(algorithms with detector)
0 10 20 30 40 50 60 70 8010
15
20
25
30
35
40
45
50
p, %
PS
NR
, dB
PSM
MPSM
15
Visual Analysis
Original image 20% salt-and-pepper noise(PSNR = 10.68 dB)
16
Visual Analysis
AMF(PSNR = 33.94 dB)
MPSM(PSNR = 36.77 dB)
17
Highly Corrupted Image Restoration
50% salt-and-pepper noise(PSNR = 8,45 dB)
MPSM(PSNR = 33.22 dB)
18
Highly Corrupted Image Restoration
80% salt-and-pepper noise(PSNR = 6.42 dB)
MPSM(PSNR = 25.83 dB)
19
Random Valued Impulse Noise Model
pprobablitywith
pprobablitywithzx
ii 1,
,
ix - corrupted image pixel values
i - original image pixel values
p - noise density
z - uniformly distributed random value
20
Random Valued Impulse Noise Model(known restoration algorithms)
ADAPTIVE CENTRAL WEIGHTED MEDIAN FILTER (ACWM)(Chen T., Wu H. Adaptive impulse detection using center-weighted median filters // IEEE Signal Processing Letters, 2001. V. 8, № 1. P. 1-3. )
• provides good results if noise density is small
SIGNAL DEPENDED RANK ORDERED MEAN FILTER (SDROM)(Abreu E., Mitra S. A signal-dependent rank ordered mean (SD-ROM) filter-a new approach for removal of impulses from highly corrupted images // Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP’95), 1995. V. 4, P. 2371-2374. )
• preserves object edges• provide relatively good visual perceived quality• removes blotches from highly corrupted images
DIRECTED WEIGHTED MEDIAN FILTER (DWM)(Dong Y., Hu S. A new directional weighted median filter for removal of random-valued impulse noise // IEEE Signal Processing Letters, 2003. V. 14, № 3. P. 193-196. )
• often loose effectiveness if noise density is high
• may blur tiny objects on image
21
Random Valued Impulse Noise Detectors Comparison
p 10% 20% 30%
missfalse-
hitmiss
false-hit
missfalse-
hit
DWM 5216 2212 8607 4217 10396 6715
ACWM 3372 1514 8134 1553 14351 2110
SDROM 3523 4501 7257 5119 10962 6749
miss – number of corrupted pixels missed by detector
false-hit – number of noise-free pixels marked by detector as corrupted
22
Proposed MPSM Algorithmfor Random Valued Impulse Noise Removal
(noise detector)
}{ ix
}{ if
DWW
}|),1({ Wiji
wi jxwxmediany
0
1
:
i
i
kk
felse
fthen
Tdkif }{ f
noise detection
result
}{ ix − input corrupted image }{ if − binary matrix
12 kwwiik yxd
23
Proposed MPSM Algorithmfor Random Valued Impulse Noise Removal
(filtration procedure)
}{}{ 0 xx }{}{ 0 ff FWW
}|{ 1 Wi
nj
ni jxmedmed
Wij
njfM 1
11
21
,
0,
1
ni
ni
ni
ni
ni
ni
ni
ni
ffxxelse
fmedxthen
WMandfif }{ f
restored image
}{ 1nix
}{ 1nif
i
nifK
0K
0K
24
Random Valued Impulse Noise Removal Results
0 2 4 6 8 10 12 14 16 18 2031
32
33
34
35
36
37
38
39
40
41
p, %
PS
NR
, dB
Median
SDROMDWM
MPSM
25
Visual Results
15% random valued impulse noise(PSNR = 17,47 dB)
SDROM(PSNR = 34,70 dB)
26
Visual Results
DWM(PSNR = 35,28 dB)
Proposed Algorithm(PSNR = 35,91 dB)
27
More…
The MPSM algorithm for random valued impulse noise removal is described in more details in the paper:
D. Kuykin, V. Khryashchev, A. Priorov. DETECTION AND RESTORATION OF RANDOM-VALUED IMPULSE NOISE CORRUPTED PIXELS
which was submitted to participate in The 2009 International Workshop on
Local and Non-Local Approximation in Image Processing (LNLA’2009)
Also this paper, MatLab code and all results can be downloaded from
http://www.piclab.ru/research/mpsm.html
28
PICLAB(www.piclab.ru)
PICLAB – is the advanced tool for image restoration and research of image processing algorithms
29
Conclusion
Proposed modified algorithm for salt-and-pepper impulse noise removal based on switching schema and rank ordered statistics provide about 1-2 dB PSNR increasing relative to another filters
Proposed MPSM algorithm for restoration of images corrupted by random valued impulse noise provides more performance in image restoration which is expressed in 0.5 dB PSNR increasing.
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