image restoration

1. MOTION BLUR IMAGE RESRORATION USING ALTERNATING
DIRECTION BALANCED REGULARIZATION FILTER
A
DISSERTATION
Presented
In partial fulfillment of the requirement for the award of degree of
MASTER OF TECHNOLOGY
IN
COMPUTER SCIENCE & ENGINEERING
Submitted by
Manoj Kumar Rajput
(0901CS13MT06)
Under the supervision of
Rajeev Kumar Singh
(Assistant Professor)
Department of Computer Science & Engineering and Information Technology
Madhav Institute of Technology & Science, Gwalior (MP) - 474005
Session 2013-2015

2. Contents
 Introduction
 Literature Review
 Proposed Methodology
 Experimental Analysis
 Conclusions and Future Scope
 References
 Publication

3. Introduction
 Image restoration [1] is a technique used to recover image from degraded image.
image may be distored due to blur and noise; blur can occur due to atmospheric
turbulence, motion of objects and camera miss-focus.
The degradation model of imagine system can be define as ,
Where, is the degraded image , in which denotes the two-dimensional linear
convolution operation, is the original image and is point spread function
is the additive noise.
 Motion blur is due to relative motion between the recording device and the scene.
When an object or the camera is moved during light exposure, a motion – blurred
image is produced.
 When the scene to be recorded translates relative to the camera at a constant
velocity under an angle of radians with the horizontal axis during the
exposure interval [3], the distortion is one-dimensional.
defining the length of motion by
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4. Continue…
 Atmospheric motion blur image restoration can be done in two stages: first stage is
restoration and second stage is post processing of image.
 The effects to atmospheric turbulence can be measured by calculating the scintillation
index [2]. This index is related to the mean and standard deviation of the intensity
distribution of image.
 The atmospheric turbulence can be categorized in two categories: rigid and non-rigid
bodies .
 In rigid bodies restoration, we take single frame image which is degraded through
turbulence and restoration techniques will be applied in degraded image, which is
known as single image deconvolution [4].
 The point spread function[11] of atmospheric turbulence motion blur image can be
described below,
The estimation of point spread function of atmospheric turbulence image is very
challenging without knowing prior knowledge of clean image[5] . So, it is difficult to
restore image using point spread function (PSF) .

5. Example of images
a) Original Lena image b) Motion blur Lena image
c) Restored Lena image
Figure 1. Effect of motion blur on image due to atmospheric turbulence

6. Literature Review
 Xie Kai and Li Tong [7] have proposed the Herr wavelet transform (HWT) in
discriminating different types of edges as well as recovering sharpness from the blurred
version, and then determines whether an image is blurred or not and up to what extent if
it is blurred. Two different schemes are proposed to estimate the motion blur
parameters. Two dimensional Gabor filter has been used to calculate the direction of the
blur. Radial basis function neural network (RBFNN) has been utilized to find the length
of the blur. Subsequently, Wiener filter has been used to restore the images. Noise
robustness of the proposed scheme is tested with different noise strengths. The blur
parameter estimation problem is modeled as a pattern classification problem and is
solved using support vector machine (SVM).
 R.Dash, P. K. SA, and B. Majhi [6] have introduced approach to estimate the motion
blur parameters using Gabor filter for blur direction and radial basis function for blur
length with sum of Fourier coefficients as features. Restoration attempts to recover an
image by modeling the degradation function and applying the inverse process. Motion
blur is a common type of degradation which is caused by the relative motion between
an object and camera. Motion blur can be modeled by a point spread function consists
of two parameters angle and length. Accurate estimation of these parameters is required
in case of blind restoration of motion blurred images. This paper compares different
approaches to estimate the parameters of a motion blur namely direction and length
directly from the observed image with and without the influence of Gaussian noise.
These estimated motion blur parameters can then be used in a standard non-blind de-
convolution algorithm.

7. Continue…
 Li and Simske [8] have introduced for atmospheric turbulence blurred images. They
have used the concept that kurtosis of an image increases with extent of blurring.
Phase structure has been utilized to analyze the impact of blurring on kurtosis. Blur
parameter is estimated after setting the search space on a trial and error basis. For
each of the estimated parameter, the image is de-blurred using a classical image
restoration technique.
 Haiyong Liao, Fang Li, and Michael K [9] have introduced a fast image restoration
method which selects the regularization parameter automatically to restore noisy
blurred images. The method exploits the generalized cross validation technique to
determine the amount regularization used in each restoration step. The regularization
parameter is updated each iteration, which increases the closeness of the restored
image towards the true image.
 F. Krahmer, Y. Lin, B. McAdoo, K. Ott, J. Wang, D. Widemann, and B. Wohlberg
[10] have focused on Radon transform for searching characteristics of motion blur in
capstan analysis. This report discusses methods for estimating linear motion blur.
The blurred image is modeled as a convolution between the original image and an
unknown point-spread

8. Proposed Methodology
The following steps are performed given below:
Step-1. Take input image.
Step-2. Apply length and angle in an image.
Step-3. Get motion blurred image and also add Gaussian noise with 0.001
density.
Step-4. Apply Gabor filters to estimate angle.
Step-5. Apply Radial basis function (RBF) to estimate length.
Step-6. Calculate point spread function (PSF) with the help of estimated
parameter.
Step-7. The image is restored using alternating direction balanced
regularization (ADBR) Filter.
Step-8. The calculate peak signal to noise ratio (PSNR), Mean square
error (MSE) and Elapsed time.

9. Continue…
Input image
Apply length and angle in image
And add Gaussian noise with
0.001 density.
Obtain Motion blurred image
Estimate angle using Gabor filter
Estimate length using Radial basis
function (RBF)
Calculate Point spread function
(PSF) with the help of estimated
parameter
Apply Alternating direction
balanced regularization (ADBR)
filter for restored image
Calculate PSNR, MSE and
elapsed time
End
Obtain Restored image
Figure 2. Flow Chart and GUI of Proposed work
Start

10. Experimental Analysis
 The analysis of result obtain by proposed methodology is done by comparing the result
of other existing methods. on the basis of two parameter, MSE and PSNR.
 In the restoration, the imperceptibility or the quality of the image is measured by using
peak signal to noise ratio (PSNR)[11]. PSNR is used to calculate the similarity in the
original image and the motion blurred image. the PSNR is calculated by using two
images one is the original image and other is the restored image. the value of the PSNR
is always greater the 40. the basic formula of PSNR is given below:
Where , MSE the mean square error between two image.
MEAN SQUARE ERROR (MSE)
 The MSE is used to measures average squared disparities between the original image
and the restored image[12]. the formula of MSE is given below. In the equation and
respectively
MSE
PSNR
2
10
255
log10

11. Continue…
Where, and are the gray scale values of original and restored image is
the size of image.
 Firstly the MSE (mean square error) will be calculated then the PSNR ( peak signal
to nose ratio) value is calculated. There fore , higher value of PSNR denotes less
distortion
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12. Apply method in different types of Image
(a) original lenna image (b) degrade image (c) restored image
(a) original Barbara image (b) degrade image (c) restored image
Figure 3. Lena image
Figure 4. Barbara image

13. Continue…
(a) original cameraman image (b) degrade image (c) restored image
(a) original Boat image (b) degrade image (c) restored image
Figure 5. Cameraman image
Figure 6. Boat image

14. Continue…
(a) original house image (b) degrade image (c) restored image
(a) original pappers image (b) degrade image (c) restored image
Figure 7. House image
Figure 8. Pappers image

15. Continue…
(a) original stick image (b) degrade image (c) restored image
(a) original group image (b) degrade image (c) restored image
Figure 9. Stick image
Figure 10. Group image

16. Continue…
 MSE (Mean Square Error) value are shown in table 1.
Table 1: Mean Square Error (MSE) value between existing and proposed method
* Motion blur parameters estimation for image restoration
# Motion Blur Image Restoration Using Alternating Direction Balanced Regularization Filter
S.No Image MSE
calculated by
Base Method
MSE
calculated by
Proposed
Method
1 Lena 0.0158 0.0090
2 Barbara 0.0087 0.0219
3 Cameraman 0.0157 0.0107
4 Boat 0.0119 0.0093
5 House 0.0149 0.0083
6 Pappers 0.0092 0.0027
7 Stick 0.0073 0.0070
8 Group image 0.0271 0.0047
#
*

17. Continue…
 Peak Signal to Noise Ratio (PSNR) value are shown in table 2.
S.No Image PSNR Value
calculated by
Base Method
PSNR value
calculate
Proposed
Method
1 Lena 66.16 68.54
2 Barbara 61.74 64.71
3 Cameraman 66.16 67.82
4 Boat 67.36 68.42
5 House 66.38 68.94
6 Pappers 68.49 73.84
7 Stick 69.50 69.67
8 Group image 63.79 71.32
Table 2: Peak Signal to Noise Ratio (PSNR) value between existing and proposed method.
* #
* Motion blur parameters estimation for image restoration
# Motion Blur Image Restoration Using Alternating Direction Balanced Regularization Filter

18. Continue…
Figure 11. Comparison between existing Method and Proposed Method MSE on different images.

19. Continue…
Figure 12. Comparison between Base Method and Proposed Method PSNR on different images.

20. Conclusion
 The restoration results in the improved visualization of the image. This work
presented an Alternating Direction Balanced Regularization Method for finding
restored image
 which is useful for enhancing the peak signal to noise ratio (PSNR) value for that
image. In this work, mean square error (MSE) value of an image decreases in such a
way that gives optimized and enhanced image. Proposed algorithm takes less
execution time as compared to existing methods.
 In this study, Gabor filter and radial basis function have been used to estimate blur
angle and blur length respectively. Performance analysis has been made on only
blurred images as well as noisy blurred images. The proposed scheme estimates the
blur parameters close to the true value. Comparative analysis demonstrates the
efficiency of the proposed method.
 This implicates the robustness of the proposed method. Both standard and real-time
images have been included in experiment. It has been observed in all cases the
proposed method provides better result. The experimental analysis of this work
shows that proposed algorithm gives better and optimized restored image.

21. Future Scope
 There is hope to build new methodology which increases peak signal to noise ratio (PSNR)
value of restored image which provides more accurate and efficient results through newly
optimization techniques.

22. References
[1] Dalong Li and Steven Simske, “Atmospheric Turbulence Degraded Image by Kurtosis
Minimization”, IEEE Geoscience and RemoteSensing Letters, vol.13, pp 63-69, Dec. 2008.
[2] R.E. Hufnagel and N.R. Stanley, “ Modulation transfer function associated with image through
turbulence media”, J. opt. Soc. Amer: A, Opt. Image Sci., vol. 54, pp 52-61, 1964.
[3] K. He, J. Sun and X. Tang, “Single image haze removal using dark channel prior”, Proc. of
CVPR, vol 15, PP 1956-1963, June 2009.
[4] D. Li, S. Simske , and R.M. Mersereau, “ Blind Image deconvolution using constrained
variance maximization”, in proc. Asilomar Conf. Signals, Syst., Comput, vol 08, pp. 1762-
1765, 2004.
[5] Luxin Yan, Mingzhi Jin, Houzhang Fang, Hai Liu, and Tianxu Zhang, “Atmospheric-
Turbulence-Degraded Astronomical Image Restoration by Minimizing Second-Order Central
moment,” IEEE Geoscience And Remote Sensing Letters, Vol. 9, pp. 672-676, July 2012.
[6] R. Dash, P. K. Sa, and B. Majhi, “RBFN based motion blur parameter estimation”, in Proc.
IEEE International Conference on Advanced Computer Control, Singapore, vol 18, PP 327-33,
Jan 2009.
[7] Xie Kai and Li Tong, “Arnoldi process based on optimal estimation of the regularisation
parameter”, In IEEE International Workshop on Imaging Systems and Techniques, vol 16, PP
340 – 343. 2009.

23. Continue…
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convolution: Motion blur estimation”, Tech Rep., Univ. Minnesota, vol 6 ,PP 478- 482, 2006.
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20th IEEE ICIP, vol 11, pp. 714–718, Sep. 2013.
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Proc. IEEE 12th Int. Conf. Comput. Vis, vol 13, pp. 2201–2208,Oct. 2009.

24. Continue…
[15]Amandeep Kaur, Vinay Chopra, “A Comparative Study and Analysis of Image
RestorationTechniques Using Different Images Formats”, International Journal for Science and
Emerging Technologies with Latest Trends, vol 8,PP 7-14,2012.
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[20]Neelamani , Choi , and Baraniuk, “Fourier-wavelet regularized de-convolution for ill conditioned
systems”, IEEE Trans. on Signal Processing, vol 14,PP 891-899,2003.

25. Publication
 Manoj Kumar Rajput, Rajeev Kumar Singh, “A Review On Image
Restoration Techniques”, International Journal Of Advanced Technology &
Engineering Research , Volume 5, Issue 6, pp. 1-7,ISSN: 2250-3536,
Nov2015. (published)
 Manoj Kumar Rajput, Rajeev Kumar Singh, “Image Restoration of
Motion Blur Image using Alternating Direction Balanced Regularization
Method”, International Journal Of Communication Systems And Network
Technologies (IJCSNT) , ISSN: 2053-6283, 2015. (Accepted)