Abstract: This paper deals with a problem of removing reflection and distortion from un-natural images. This will effected in the quality of images. Reflection happens when there is the variation in direction of a wave front at an interface between two different media so that the wave front returns into the medium from which it originated. The law of reflection describes for specular reflection the angle at which wave is reflected equals the angle at which it is incident on the surface. Mirrors exhibit specular reflection. In photograph Distortion will happens when either the properties of the lens or the position of the camera relative to the subject. Here the input contains multiple polarized images with different polarizer angles. The output consists of high quality distortion and reflection separation from images. In this paper proposed a Quality Assessment method Scheme (QAMS) for removing both reflection and distortion from images. Using this QAMS method, the quality of the image can be improved by measuring PSNR and Error Rate

Asha Susan Prakash

Manimurugan S

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ISSN: 2277-3754 
    ISO 9001:2008 Certified 
  International Journal of Engineering and Innovative Technology (IJEIT) 
             Volume 2, Issue 3, September 2012 
 
 
287 
A Simple Method for Removing Reflection and 
Distortion from a Single Image 
Manimurugan.S, Asha Susan Prakash
       
     Abstract: This paper deals with a problem of removing 
reflection and distortion from un-natural images. This will 
effected in the quality of images. Reflection happens when 
there is the variation in direction of a wave front at an 
interface between two different media so that the wave front 
returns into the medium from which it originated. The law of 
reflection describes for specular reflection  the angle at which 
wave is reflected equals the angle at which it is incident on the 
surface. Mirrors exhibit specular reflection. In photograph 
Distortion will happens when either the properties of the lens 
or the position of the camera relative to the subject. Here the 
input contains multiple polarized images with different 
polarizer angles. The output consists of high quality distortion 
and reflection separation from images. In this paper proposed 
a Quality Assessment method Scheme (QAMS) for removing 
both reflection and distortion from images. Using this QAMS 
method, the quality of the image can be improved by 
measuring PSNR and Error Rate. 
 
   Keywords: Image Enhancement, Distortion Separation, 
Reflection Separation. 
 
I. INTRODUCTION 
 This paper deals with a problem of reflection and 
distortion separation in photography. The issue of  this 
reflection and distortion separation arises naturally in our 
everyday life when a desired scene contains another scene 
reflected off a transparent or semi reflective medium. One 
of the common examples for this is when we took 
photographs through windows or taking the images of an 
object which are placed inside a glass. When we took 
such kind of images there will be reflection occurs. 
Sometimes the camera may be shake when we take 
photographs so the image will be distorted or blurred. 
Distortion will happens when either the properties of the 
lens or the position of the camera relative to the subject. 
One type of distortion is caused when the camera is keen 
at an extreme angle to the subject. The figure 1 below 
shows an example or perspective distortion and also 
known as leaning of the subject. In the Fig 1 church 
appears to be leaning away from the camera.  
 
Fig 1 Perspective Distortion 
Another type of distortion is called as barrel Distortion. 
Inside a lens there is a round glass element, with the side 
effect of this type of glasses occurs barrel distortion. Due 
to the effect of bending light more near the edges of the 
lens then we happenstance near the center of the lens. 
When the lens diameter is smaller it will get more 
extreme effect of barrel Distortion becomes. The fig .2 (a) 
and (b) shows an example of barrel distortion 
 
Fig .2  (A) (B)Shows Barrel Distortion 
Depending on the nature of the interface, reflection of 
light is either specular or mirror like or diffuse (retaining 
the energy, but losing the image). Furthermore, 
the phase of the reflected wave is retained if the interface 
is placed between a dielectric and a conductor, otherwise 
if the interface is between two. The most common model 
for specular light reflection is provided by the mirror, and 
typically consists of a glass sheet with a metallic coating 
where the reflection actually occurs. With the help of 
suppression of wave propagation beyond their skin depths 
Reflection can be enhanced in metals. Reflection also 
happens at the surface of  transparent  media, such 
as glass or water.             A polarization glasses is used by 
the polarized 3D system to create the illusion of three-
dimensional images by restricting the light that reaches 
each eye, This is an example of stereoscopy.  
The image with reflection can be described by a linear 
superposition of two layers: the background layer from 
the scene beyond the glass and the reflection layer from 
the scene reflected by the glass. Decomposing the 
degraded input image into two layers is an ill-posed 
problem since there are an infinite number of ways to 
decompose an image. Fortunately, the reflection layer is a 
polarized image [2]. One of the common examples to 
reduce the effect of reflection is to place a polarizer in 
front of the camera lens to filter out the polarized light 
coming from reflection. However, the amount of 
polarization depends on the angle of incident light. In 
most cases, the reflected light is only partially polarized. 
Previous work of the polarization-based approaches are 
solved by simply collecting polarized pixel values to 
explored the reflection separation problem. In Ohnishi et 
 
 
 
ISSN: 2277-3754 
    ISO 9001:2008 Certified 
  International Journal of Engineering and Innovative Technology (IJEIT) 
             Volume 2, Issue 3, September 2012 
 
 
288 
al. [3], proposed the reflection layer by computing the 
image difference between the maximum intensity image 
and the minimum intensity image and he background 
layer as minimum intensity image over different polarizer 
angles. However with partial polarization, a polarizer 
cannot fully separate reflection and in there recovered 
background weak reflection may still remain. It is also 
common that reflection is reduced in certain parts of the 
image but remains in other parts, when we change the 
rotation angle of the polarizer. 
By analyzing the polarized images, the remaining 
reflection can be further reduced. While improving the 
accuracy and efficiency based on sparsity of large image 
gradients, Bronstein et al. [5] proposed the ICA approach 
to allow multiple polarized images, Farid and Andelson 
[7] generalized a method, which can separate reflection 
from two polarized images without using such prior 
knowledge based on independent component analysis 
(ICA).In Schechner et al. [4], [6] based on physical 
analysis of polarization, reflection are separated. Their 
method assumed some prior knowledge about the scene, 
such as an angle of incidence and a pair of polarizer 
angles that maximize and minimize reflection, which are 
hard to be measured directly in general.. Using [1 ]it can 
only completely remove the reflection from an image. By 
combining this reflection separation algorithm [1] and the 
use of a distortion separation algorithm, we can remove 
both reflection and distortion from a single image. Here it 
uses a QAM scheme for removing the reflection and 
distortion from an image. The fig 3(a) 3(b) shows an 
image containing both reflection and distortion. We begin 
our study of reflection and distortion separation by 
utilizing the effect of reflection under different rotation 
angles of a polarizer. Our approach uses two algorithm to 
separate both reflection and distortion. Here we use two 
algorithm for removing the reflection and distortion from 
a single image. 
 
Fig. 3 (A) and (B) Shows the Reflection and Distortion on 
the Surface of Building 
 
II.PROPOSED SYSTEM 
In this approach we are removing the reflection and 
distortion from a single image using the two algorithms. 
The algorithms used for this purpose are reflection 
separation algorithm [1] and distortion Separation 
Algorithm. Here first we check whether there is any 
reflection in our input image. If there is any reflection in 
the image removes it using our reflection separation 
algorithm otherwise leaves it.  Then we check whether 
any distortion in the image. if any distortion in the image 
remove it using the distortion separation algorithm and 
otherwise we get the same output what we give as our 
input  or a reflection separated image, if there is any 
reflection  finally we get the desired output. .The 
algorithm for distortion tries to calculate two main 
parameters of the distortion K1 and K2.This is based on 
an extremely simple search loop finding one parameter(k) 
at a time is either k1 or k2.Fig.3 shows the flow chart of 
proposed system. 
Algorithm 1: Procedures for reflection separation [1] 
Input: I1, . . . , IN  
Output: α1, . . . , αN,R,B 
Construct the Gaussian image pyramid. 
For each level, from coarse to fine, in the multi-scale 
pyramid, do: 
Compute the mask image and the reflection guide map. 
If the current scale is the coarsest scale, 
Initialize αi  
else: 
Up-sample the results of αi, R and B. 
Evaluate the regularization weights λαi , λR and λB. 
end if 
For a fixed number of iterations do: 
Estimate (R,B) with i fixed. 
Estimate αi with (R,B) fixed. 
end for 
end for 
 
 
 
Algorithm 2: procedure for Distortion Separation 
Input:I 
Output:B 
Construct the Gaussian  image Pyramid 
While(step>minstep) 
{ 
Calculate_error_at (K-step) 
Calculate_error_at (K+step) 
Calculate_error_at (K) 
If(K is smallest) 
{ 
Step:=step/4; 
} 
else 
{ 
If(K-step was better) 
k:=K-step 
else 
K:=K+step 
}} 
 
 
 
ISSN: 2277-3754 
    ISO 9001:2008 Certified 
  International Journal of Engineering and Innovative Technology (IJEIT) 
             Volume 2, Issue 3, September 2012 
 
 
289 
III.FLOW CHART 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig.3 Flow Chart of Proposed System 
 
IV. EXPERIMENTS AND RESULT 
The proposed approach can be used to remove 
reflection and distortion from a single image. This helps 
to increase the quality of image. This method is applied to 
four different images. It shows a better PSNR value when 
compared to other. The fig 4(a) shows an image 
containing both reflection and distortion. Using this 
QAMS method it can simply remove. Fig 4(a) shows the 
output of 5(e) after removing the reflection and distortion. 
Similarly Fig 4(b) 4(c) 4(d) is the input and 5(f) 5(g) 5(h) 
is the output after using the QAMS method. The below 
table shows the PSNR value and error rate of four various 
images containing both reflection and distortion. 
Table 1: The accuracy of Images 
 
Input 
image 
Size of 
image 
before 
processing 
Size 
of  
image 
after 
processi
ng 
 
PSN
R 
Erro
r rate 
 
Building 
155KB 202  
KB 
58.0
6 
1.09 
Nature 1263KB 2529
KB 
59.6
4 
2.23 
Floor 48.8KB 91.2K
B 
57.8
9 
1.58 
Man 223KB 256 
KB 
58.3
7 
1.87 
 
The Graph shows the PSNR value of various images 
0
0.5
1
1.5
2
2.5
Building Nature Floor Man
 
The Graph shows the Error Rate value of various images 
  
                   (a)                                     (b)                                  (c)                                    (d) 
Fig.4 (A), (B), (C), and (D) Shows Image Containing Both Reflection and Distortion 
Input image 
Reflection 
occurred? 
Reflection 
separation 
algorithm 
Original 
input image 
Output 
image 
Distortion 
occurred? 
Distortion 
separation 
algorithm 
Remove 
distortion 
High Quality 
Image 
High 
Quality 
Image 
Yes No 
No yes 
Remove 
reflection 
 
 
 
ISSN: 2277-3754 
    ISO 9001:2008 Certified 
  International Journal of Engineering and Innovative Technology (IJEIT) 
             Volume 2, Issue 3, September 2012 
 
 
290 
 
                 (e)                                      (f)                             (g)                                     (h) 
Fig. 5 (E) ,(F), (G) and (H) Shows The Result  After Removing Reflection and Distortion Using Algorithms 
 
V. CONCLUSION 
The contribution of this paper is removing reflection 
and distortion from a single image. Here a reliable 
method is used for removing the reflection and distortion 
from single images. For this we use two algorithms for 
removing both the contents from the images. The image 
can be either real images or synthetic images. The input 
image consists both of this. The output is high quality 
reflection and distortion separated images. Here we 
propose a method called QAMS.Using this method the 
quality image can be measured by PSNR value and by 
calculating the error rate. 
 
ACKNOWLEDGEMENT 
  We would like to express our gratitude to all those 
who gave us the possibility to complete this paper 
 
REFERENCES 
[1] Naejin Kong Yu-Wing Tai Sung Yong Shin “High Quality 
reflection separation using polarized images IEEE 
Transaction on Image Processing April 2011. 
[2] H. Fujikake, K. Takizawa, T. Aida, H. Kikuchi, T. Fujii, 
and M. Kawakita, “Electrically-controllable liquid crystal 
polarizing filter for eliminating reflected,” Optical Review, 
vol. 5, no. 2, pp. 93–98, 1998. 
[3] N. Ohnishi, K. Kumaki, T. Yamamura, and T. Tanaka, 
“Separating real and virtual objects from their overlapping 
images,” in Proc. European Conference on Computer 
Vision (ECCV), vol. 1065, 1996, pp. 636–646S.  
[4] Y. Y. Schechner, J. Shamir, and N. Kiryati, “Polarization-
based decor relation of transparent layers: The inclination 
angle of an invisible surface,” in Proc. IEEE International 
Conference on Computer Vision (ICCV) 1999, pp. 814–
819. 
[5] A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, and Y. 
Y. Zeevi “Sparse ICA for blind separation of transmitted 
and reflected images,” aInt. J. Imaging Systems and 
Technology, vol. 15, no. 1, pp. 84–91, 2005. 
[6] Y. Y. Schechner, J. Shamir, and N. Kiryati, “Polarization 
and statistical analysis of scenes containing a semi 
reflector,” J. Opt. Soc. Am., vol. 17, no. 2, pp. 276–284, 
Feb. 2000. 
[7] H. Farid and E. Adelson, “Separating reflections from 
images by use of independent components analysis,” J. 
Opt. Soc. Am., vol. 16, pp. 2136–2145, 1999. 
 
AUTHOR BIOGRAPHY 
 
 Dr.S.Manimurugan completed his Bachelor’s 
Degree from Anna University and he received his 
Master’s Degree from Karunya University. He 
was highly commended for his work in Image 
Processing and Information Security, for which 
he was honored with a PhD from Anna 
University. 
 
 
Ms.Asha Susan Prakash received the B.Tech. 
Degree in computer science and engineering in 
2010 from the M.G University, College MBC. 
She is currently doing M.Tech Research in the 
area of Image Processing at the department of 
Computer Science and Engineering, Karunya 
University. 
 
 
 
 


A Simple Method for Removing Reflection and Distortion from a Single Image

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A Simple Method for Removing Reflection and Distortion from a Single Image

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