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Image Magnification based on the Human Visual Processing
15
Image Magnification based on the Human Visual
Processing
Sung-Kwan Je
1
, Kwang-Baek Kim
2
, Jae-Hyun Cho
3
and Doo-Heon Song
4
1
Dept. of Computer Science, Pusan National University
2
Dept. of Computer Engineering, Silla University
3
Dept. of Computer Engineering, Catholic University of Pusan
4
Dept. of Computer Game and Information, Yong-in SongDam College
Korea
1. Introduction
Image magnification is among the basic image processing operations and has many
applications in a various area. In recent, multimedia techniques have advanced to provide
various multimedia data that were digital images and VOD. It has been rapidly growing
into a digital multimedia contents market. In education, many researches have used e-
learning techniques. Various equipments - image equipments, CCD camera, digital camera
and cellular phone are used in making multimedia contents. They are now widespread
and as a result, computer users can buy them and acquire many digital images as desired.
This is why the need to display and print them also increases (Battiato & Mancuso, 2001;
Battiato et al., 2002).
However, such various images with optical industry lenses are used to get high-resolution.
These lenses are not only expensive but also too big for us to carry. So, they are using the
digital zooming method with the lenses to solve the problem. The digital zooming method
generally uses the nearest neighbor interpolation method, which is simpler and faster than
other methods. But it has drawbacks such as blocking phenomenon when an image is
enlarged. Also, to improve the drawbacks, there exist bilinear interpolation method and the
cubic convolution interpolation commercially used in the software market. The bilinear
method uses the average of 4 neighborhood pixels. It can solve the blocking phenomenon
but brings loss of the image like blurring phenomenon when the image is enlarged. Cubic
convolution interpolation improved the loss of image like the nearest neighbor interpolation
and bilinear interpolation. But it is slow as it uses the offset of 16 neighborhood pixels
(Aoyama & Ishii, 1993; Candocia & Principe, 1999; Biancardi et al., 2002).
A number of methods for magnifying images have been proposed to solve such problems.
However, proposed methods on magnifying images have the disadvantage that either the
sharpness of the edges cannot be preserved or that some highly visible artifacts are
produced in the magnified image. Although previous methods show a high performance in
special environment, there are still the basic problems left behind. Recently, researches on
Human vision processing have been in the rapid progress. In addition, a large number of
models for modeling human vision system have been proposed to solve the drawbacks of
Source: Vision Systems: Applications, ISBN 978-3-902613-01-1
Edited by: Goro Obinata and Ashish Dutta, pp. 608, I-Tech, Vienna, Austria, June 2007
Open
Access
Database
www.i-techonline.com Vision Systems: Applications
264
machine vision such as object recognition and object detection (Suyung, 2001). In the field of
optical neural, many researches have been conducted in relation with physiology or biology
to solve the problem of human information processing. Features of biological visual systems
at the retinal level serve to motivate the design of electronic sensors. Although commercially
available machine vision sensors begin to approach the photoreceptor densities found in
primate retinas, they are still outperformed by biological visual systems in terms of dynamic
range, and strategies of information processing employed at the sensor level (Shah &
Levine, 1993). However, most of the retina models have focused only on the characteristic
functions of retina by generalizing the mechanisms, or for researcher's convenience or even
by ones intuition. Although such a system is efficient to achieve a specific goal in current
environment, it is difficult to analyze and understand the visual scene of a human body. The
current visual systems are used in very restricted ways due to the insufficiency of the
performance of algorithms and hardware.
Recently, there are many active researches to maximize the performance of computer vision
technology and to develop artificial vision through the modeling of human visual
processing. Artificial vision is to develop information processing procedures of the human
visual system based on the biological characteristics. Compared with the machine vision
technology, it can be effectively applied to industry. By investing over 20 billion yen
between 1997 and 2016, Japan is conducting research on the areas of machine intelligence,
voice recognition and artificial vision based on the information processing mechanism of the
brain. By the National Science Foundation (NSF) and the Application of Neural Networks
for Industries in Europe (ANNIE), America and Europe are also conducting research on
artificial vision, as well as artificial intelligence and voice recognition using the modeling of
the brain's information processing (Dobelle, 2000).
This paper presents a method for magnifying images that produces high quality images
based on human visual properties which have image reduction on retina cells and
information magnification of input image on visual cortex. The rest of this paper is
organized as follows. Section 2 presents the properties on human visual system and related
works that have proposed for magnifying image. Section 3 presents our method that extracts
the edge information using wavelet transform and uses the edge information base on the
properties of human visual processing. Section 4 presents the results of the experiment and
some concluding remarks are made in Section 5.
2. Related works and human visual processing
2.1 Related works
The simplest way to magnify images is the nearest neighbor interpolation by using the pixel
replication and basically making the pixels bigger. It is defined by equation (1). However,
the resulting magnified images have a blocking phenomenon (Gonzalez & Richard, 2001).
( )
( )
( )
image
magnified
a
is
j
i,
Z
where
,
j
int
l
,
i
int
k
integer
,
j
,
i
,
l
,
k
I
j
,
i
Z
=
=
2
2
0
(1)
Other method is the bilinear interpolation, which determines the value of a new pixel based
on a weighted average of the 4 pixels in the nearest
2
2
× neighbourhood of the pixels in the
original image (Gonzalez & Richard, 2001). Therefore this method produces relatively Image Magnification based on the Human Visual Processing
265
smooth edges with hardly any blocking and is better than the nearest neighbor but appears
blurring phenomenon. It is defined as equation (2).
( )
( )
(
)
( ) (
)
[
]
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( )
(
)
( ) (
)
[
]
l
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k
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,
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k
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i
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1
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1
2
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+
=
+
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+
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(2)
More elaborating approach uses cubic convolution interpolation which is more
sophisticated and produces smoother edges than the bilinear interpolation. Bicubic
interpolation uses a bicubic function using 16 pixels in the nearest
4
4
× neighborhood of the
pixel in the original image and is defined by equation (3). This method is most commonly
used by image editing software, printer drivers and many digital cameras for re-sampling
images. Also, Adobe Photoshop offers two variants of the cubic convolution interpolation
method: bicubic smoother and bicubic sharper. But this method raises another problem that
the processing time is too long due to the computation for the offsets of 16 neighborhood
pixels (Keys, 1981).
(
)
(
)
<
+ < +
+ +
=
elsewhere
,
x
,
a
x
a
x
a
x
a
x
,
x
a
x
a
)
x
(
f
0
2
1
4
8
5
1
0
1
3
2
2
3
2
3
where a=0, or -1
(3)
Recently, research on interpolation images taking into account the edges has gained much
attention. (Salisbury et al., 1996) proposed methods that search for edges in the input images
and use them to assure that the interpolation does not cross them. The problem is how to
define and find the important edged in the input image.
Other edge-adaptive methods have been proposed by (Li & Orchard, 2001). The commercial
software Genuine Fractals also used an edge adaptive method to magnify images, but the
details of the algorithm are not provided. Currently, the methods presented in (Muresan &
Parks, 2004) are the most widely known edge-adaptive methods. They can well enough avid
jagged edges, but have limitation that they sometimes introduce highly visible artifacts into
the magnified images, especially in areas with small size repetitive patterns (Johan &
Nishita, 2004).
In section 3, we will propose an efficient method by image reduction and edge enhancement
based on the properties on human visual processing.
2.2 Human visual processing
In the field of computer vision, many researches have been conducted in relation with edge
information to solve the problem of magnification. Image information received from retina
in Human visual system is not directly transmitted to the cerebrum when we recognize it.
This is why there are many cells playing in Human visual system (Bruce, 2002).
First, the visual process begins when visible light enters the eye and forms images on