Given an image with significant portions missing or damaged. Dynamically detect the damaged regions to be inpainted. Reconstitute missing regions with data consistent with the rest of the image. Proposed a method which restore damaged area of the image reducing processing time without blurring output.

1. International Conference on Frontiers of Image Processing (ICFIP 2017)
1
Date : March 3-5, 2017
Paper ID : 10
Location : Katmandu, Nepal
Presented by:
Md. Shohel Rana
AN ENHANCED MODEL FOR INPAINTING ON DIGITAL
IMAGES USING DYNAMIC MASKING

2. Motivation and Contribution
• Given image with significant portions missing or damaged
• Dynamically detect the damaged regions to be inpainted
• Reconstitute missing regions with data consistent with the rest of the image
• Proposed a method which restore damaged area of the image reducing processing time
without blurring output
International Conference on Frontiers of Image Processing (ICFIP 2017) 2

3. Inpainting
• The technique of reconstruction of missing or damaged portion of the images
• Techniques are in use for various applications like removal of scratches, restoring
damaged/missing portions, or removal of object from the image
• Inpainting technique can be categorized as follows:
 Convolution Filter based method
 Partial Differential Equation (PDE)
 Others Algorithm
• In general all convolution based methods provide good results
• The convolution based equation is as follows
Where Iout is inpainted image, I is the input image W is the Mask and M x N is size of the Image
International Conference on Frontiers of Image Processing (ICFIP 2017) 3
∑∑= =
=
M
i
N
j
out jiWjiIjiI
0 0
),(),(),(

4. Convolution based inpainting
• Convolution of two functions f(x) and g(x)
International Conference on Frontiers of Image Processing (ICFIP 2017) 4

5. Various inpainting techniques on image
• Bertalmio’s algorithm: The final equation for image I with inpainting region
Ω
Where
• Oliveira Algorithm: Based on the Bertalmio method, Oliviera et al. have
proposed an inpainting algorithm that relies exclusively on diffusion
Where the values of a, b, and c for both kernels are 0.073235, 0.176765, and 0.125 respectively
International Conference on Frontiers of Image Processing (ICFIP 2017) 5
( ) ( )
Ω∈∀∆∆+=+
),(|,),(|),(),(1
jijiItjiIjiI nnnn
β
|),,(|
),,(
),(:),(
njiN
njiN
jiLji nn



⋅= δβ
∑∑
∑∑
= =
= =
=
=
M
i
N
j
out
M
i
N
j
jiWjiIjiI
jiWjiIjiI
0 0
/
0 0
/
),(2).,(),(
),(1).,(),(

6. Various inpainting techniques on image
• Hadhoud, Moustafa, and Shenoda’s Algorithm: They have proposed
an improvement of Oliveira’s method with reducing processing time with
convoluting the region with averaging filter has a zero weight at bottom
right corner instead of center.
Where the values of a, b, and c for both kernels are 0.073235, 0.176765, and 0.125
respectively
International Conference on Frontiers of Image Processing (ICFIP 2017) 6
∑∑
∑∑
= =
= =
=
=
M
i
N
j
out
M
i
N
j
jiWjiIjiI
jiWjiIjiI
0 0
/
0 0
/
),(2).,(),(
),(1).,(),(

7. Various inpainting techniques on image
• H. Noori, S. Saryazdi, H. Nezamabadi: uses the gradient of known
pixels in the neighborhood of a missed pixel to compute weights in
convolving mask W(x) by proposing a function F(x).
Where k presents the pixel position, x is gradient value of the current pixel in the
image, α is a parameter giving an estimation of the missed pixel gradient control
the softness of propagation and f'(p) is estimated value, f(k) is value of a known
pixel, n is the number of known pixel in the current neighborhood.
International Conference on Frontiers of Image Processing (ICFIP 2017) 7


















≥
≤≤−
≤−
=
α
α
α
α
α
α
||0
||
2
)1(
2
||)(1
)( 2
2
xif
xif
x
xif
x
xF
)(
1
)( kxF
n
xw =
)())()())(1()( 11
/
kfkwpfkwpf
n
k
n
k ∑∑ −−
+−=

8. Image measurement matrices
• RMSE: The root mean square error
• PSNR: Peak Signal to Noise Ratio is computed by
International Conference on Frontiers of Image Processing (ICFIP 2017) 8
),,.
1
(
1 1
2
)(∑∑ −
= =
=
M
i
N
j
yx jijiMN
RMSE
)max(.20
2
10
log RMSEPSNR g=

9. Proposed method
In case of proposed method
•Inpaint damaged portion of the image without blurring output with taking
less time
•Dynamically detect the inpainted region with removing image’s noise
•Use only one Kernel/Mask where Oliveira method uses two kernels and
replacing the values of kernel
International Conference on Frontiers of Image Processing (ICFIP 2017) 9
0.080000 0.170000 0.080000
0.170000 0.080000 0.170000
0.080000 0.170000 0

10. Proposed method
1. Input Image with damage information
2. Color Image Converted in to Gray scale
3. Filter the image to remove noise using median filter
4. Find edge of Imid using “Canny Edge Detector” and smoothing image
to reduce the number of connected components producing Icanny
International Conference on Frontiers of Image Processing (ICFIP 2017) 10
)(
)(
]:1[]:1[
II
WI
W
sortmid
nsort
n
mid
sort
MNIF
=
=
=

11. Proposed method
5. Resize the image to produce mask image Imask which gives the target region
to be inpainted from Icanny
6. Calculate connected components to extract all the connected components.
7. Create 3x3 Window/kernel W and convolve the image using mask image and
following equation by checking damage using
8. Print Output and compute execution time
International Conference on Frontiers of Image Processing (ICFIP 2017) 11
∑∑= =
=
M
i
N
j
out jiWjiIjiI
0 0
),().,(),(

12. Experiment and result
International Conference on Frontiers of Image Processing (ICFIP 2017) 12

13. Experiment and result (CONT.)
International Conference on Frontiers of Image Processing (ICFIP 2017) 13
Figure/
Method
Oliveira
(seconds)
Noori
(seconds)
Hadhoud
(seconds)
Proposed
(seconds)
Fig-a 98.2739 86.0736 9.6012 27.0833
Fig-b 42.7693 37.4856 7.1990 15.2771
Fig-c 77.6711 63.4485 9.0671 21.4831
Figure/
Method
Oliveira Noori Hadhoud Proposed
Fig-a 12.0129 7.0215 12.9902 13.2219
Fig-b 12.7494 9.1248 14.4092 15.1507
Fig-c 15.1130 6.2251 13.1350 13.0310
TABLE 1: COMPARISON STUDY OF EXECUTION TIME OF PROPOSED METHOD
WITH OTHERS
TABLE 2: COMPARISON STUDY OF PSNR OF PROPOSED METHOD WITH
OTHERS

14. Conclusion and future work
• It has been demonstrated that proposed technique is capable of restoring
damaged/occluded 2D image
• It gives best result among those algorithms with reducing time without blurring images
• Used image extension was “.jpeg”
• It can be used in 3D ultrasound images as well as in CT, MRI, X-RAY and so on.
International Conference on Frontiers of Image Processing (ICFIP 2017) 14