16-09-2016, 02:04 PM
[attachment=71108]
Abstract
Many paintings, especially old ones, suffer from breaks in the
substrate, the paint, or the varnish. These patterns are usually
called cracks and be caused by aging, drying and mechanical
factors. The appearance of cracks on painting deteriorates the
perceived paintings quality. One can use digital image processing
techniques to detect and eliminate the cracks on the digitized
paintings. The main objective of this study is to present the
digital image processing technique that can be applied to the
virtual restoration of artistic paintings which serves many
purposes. The methods implemented on this paper are based on
studying the digital image processing technique used for cracks
identification and removal. Mat lab is used to build the code
required to process and analyze the data. One of the most
important results obtained in this paper focuses on separating the
cracks and applying interpolations techniques for the restoration
of the digitized painting.
1. Introduction
An image is the representation of a two dimensional
functions as a finite set of digital value, called picture
elements, each of which has a particular location. For each
pixel, there is an associated number knows as digital
number or sample, which dictates the color and brightness
for that particular pixel. So the image can be defined as a
two- dimensional functions, f(x, y), where x and y are
spatial (plane)” coordinates, and the amplitude of f at any
pair of coordinates(x, y) is called the intensity or gray level
of the image at that point [1].
A digital image is composed of a finite number of elements
called pixel, each of which has a particular location and
value. This mean that x, y and the intensity values of f are
all finite and discrete quantities [2].
Almost all graphics software deals with some ‘real or
painted’ images that are captured using digital cameras or
flatbed scanners. The image is needed in digital form; to
transform a continuous tone painted picture into digital
form requires a digitizer. The two functions of the digitizer are sampling and quantizing. Sampling captures evenly
spaced data points to represent a digitized image. Since
these data points are to be stored in a computer, they must
be converted to a binary form. Quantization assigns each
value a binary number [3].
Computer image have been “digitized”, a process which
converts the real word color painted picture to be numeric
computer data consisting of rows and columns of millions
of colors samples measured from the original painted
picture. In either case the image quality, color, brightness
and the darkness of each tine area seen by a sensor is
“sampled” meaning the color value of each value areas is
mastered and recorded as a numeric value which represents
the color there. This process is called digitized the paint
image. The data is organized into the same rows and
columns to retain the location of each actual tiny paint
picture area. The main cause of cracks is the ageing, drying
and mechanical factors and they appear due to the missing
or damaged of pixel painting areas [4-5].
The appearance of cracks on paintings deteriorates the
perceived image quality. However, one can use digital
image processing techniques to detect and eliminate the
cracks on digitized paintings. The existing knowledge and
understanding of crack detection in digitizing painting
using digital signal processing techniques indicates that
more extensive study is mandatory.
i. Cracks Detection
Some researchers studies focus on paintings which suffer
from breaks. These patterns are usually called cracks
which result from non-uniform contraction in the canvas
or wood-panel support of the painting that stresses the
layers of painting. Drying cracks are usually caused by the
evaporation of volatile paint components and the
consequent shrinkage of the paint. Mechanical cracks
result from painting deformations due to external causes,
e.g. vibrations and impacts. The appearance of cracks on
painting deteriorates the perceived image quality.
However, one can use digital image processing techniques to detect and eliminate the cracks on digitized paintings. A
method for the restoration of cracks on digitized paintings,
which adapts and integrates a number of images
processing and analysis tools is proposed in this paper.
The methodology is an extension of the crack removal
framework presented in the state of art. The technique
consists of crack detection, classification and filling.
Cracks usually have low luminance and thus can be
considered as local intensity minima with rather elongated
structural characteristics. Therefore, a crack detector can
be applied on the luminance component of an image and
should be able to identify such minima. A crack detection
procedure based on top-hat transform is proposed on this
paper. The top-hat transform technique defined in Eq1.
[2,5-6]:
?(?) = ?(?) − ?(?) (1)
where ?(?) represents the luminance component of the
image, ?(?) is original negated image and ?(?) is the
opening of the image fR R(x), B is the structuring element and
n represents the number of times the dilation is made i.e.
? = ?(?)?(?) … … . . (? ?) (2)
The opening ?(?) of function is a low-pass nonlinear
filter that erases all peaks (local maxima) in which the
structuring element ’nB’ cannot fit, Thus, the image ‘ffnB’
contains only those peaks and no background at all,
hence, the cracks which are the local minima segmented by
talking the top hat transform of the negated image.
Figure1shows the original image with cracks and Figure2
shows the image after the top hat transforms
Abstract
Many paintings, especially old ones, suffer from breaks in the
substrate, the paint, or the varnish. These patterns are usually
called cracks and be caused by aging, drying and mechanical
factors. The appearance of cracks on painting deteriorates the
perceived paintings quality. One can use digital image processing
techniques to detect and eliminate the cracks on the digitized
paintings. The main objective of this study is to present the
digital image processing technique that can be applied to the
virtual restoration of artistic paintings which serves many
purposes. The methods implemented on this paper are based on
studying the digital image processing technique used for cracks
identification and removal. Mat lab is used to build the code
required to process and analyze the data. One of the most
important results obtained in this paper focuses on separating the
cracks and applying interpolations techniques for the restoration
of the digitized painting.
1. Introduction
An image is the representation of a two dimensional
functions as a finite set of digital value, called picture
elements, each of which has a particular location. For each
pixel, there is an associated number knows as digital
number or sample, which dictates the color and brightness
for that particular pixel. So the image can be defined as a
two- dimensional functions, f(x, y), where x and y are
spatial (plane)” coordinates, and the amplitude of f at any
pair of coordinates(x, y) is called the intensity or gray level
of the image at that point [1].
A digital image is composed of a finite number of elements
called pixel, each of which has a particular location and
value. This mean that x, y and the intensity values of f are
all finite and discrete quantities [2].
Almost all graphics software deals with some ‘real or
painted’ images that are captured using digital cameras or
flatbed scanners. The image is needed in digital form; to
transform a continuous tone painted picture into digital
form requires a digitizer. The two functions of the digitizer are sampling and quantizing. Sampling captures evenly
spaced data points to represent a digitized image. Since
these data points are to be stored in a computer, they must
be converted to a binary form. Quantization assigns each
value a binary number [3].
Computer image have been “digitized”, a process which
converts the real word color painted picture to be numeric
computer data consisting of rows and columns of millions
of colors samples measured from the original painted
picture. In either case the image quality, color, brightness
and the darkness of each tine area seen by a sensor is
“sampled” meaning the color value of each value areas is
mastered and recorded as a numeric value which represents
the color there. This process is called digitized the paint
image. The data is organized into the same rows and
columns to retain the location of each actual tiny paint
picture area. The main cause of cracks is the ageing, drying
and mechanical factors and they appear due to the missing
or damaged of pixel painting areas [4-5].
The appearance of cracks on paintings deteriorates the
perceived image quality. However, one can use digital
image processing techniques to detect and eliminate the
cracks on digitized paintings. The existing knowledge and
understanding of crack detection in digitizing painting
using digital signal processing techniques indicates that
more extensive study is mandatory.
i. Cracks Detection
Some researchers studies focus on paintings which suffer
from breaks. These patterns are usually called cracks
which result from non-uniform contraction in the canvas
or wood-panel support of the painting that stresses the
layers of painting. Drying cracks are usually caused by the
evaporation of volatile paint components and the
consequent shrinkage of the paint. Mechanical cracks
result from painting deformations due to external causes,
e.g. vibrations and impacts. The appearance of cracks on
painting deteriorates the perceived image quality.
However, one can use digital image processing techniques to detect and eliminate the cracks on digitized paintings. A
method for the restoration of cracks on digitized paintings,
which adapts and integrates a number of images
processing and analysis tools is proposed in this paper.
The methodology is an extension of the crack removal
framework presented in the state of art. The technique
consists of crack detection, classification and filling.
Cracks usually have low luminance and thus can be
considered as local intensity minima with rather elongated
structural characteristics. Therefore, a crack detector can
be applied on the luminance component of an image and
should be able to identify such minima. A crack detection
procedure based on top-hat transform is proposed on this
paper. The top-hat transform technique defined in Eq1.
[2,5-6]:
?(?) = ?(?) − ?(?) (1)
where ?(?) represents the luminance component of the
image, ?(?) is original negated image and ?(?) is the
opening of the image fR R(x), B is the structuring element and
n represents the number of times the dilation is made i.e.
? = ?(?)?(?) … … . . (? ?) (2)
The opening ?(?) of function is a low-pass nonlinear
filter that erases all peaks (local maxima) in which the
structuring element ’nB’ cannot fit, Thus, the image ‘ffnB’
contains only those peaks and no background at all,
hence, the cracks which are the local minima segmented by
talking the top hat transform of the negated image.
Figure1shows the original image with cracks and Figure2
shows the image after the top hat transforms