Embed Size (px)
Citation preview
An Adaptive System for Gray Scale to RGB Image Conversion
1.Apurva B. Parandekar
, M.E Scholar, Sipna College of Engineering &
Technology, Amravati
2. Prof. Shital. S. Dhande
Associate Professor, Sipna College of Engineering &
Technology, Amravati
Abstract: We introduce a general technique for making colorless
images into colored one. To achieve this we are
introducing a technique for predicting color of a
particular image. This technique helps in adding
chromatic values to a colorless image and
sophisticated measure for color transfer. Rather than
choosing the entire color from the source to the target
image, we transfer RGB colors from a palette to color
gray scale components, by matching difference
information between the images. Particular emphasis is
placed on using color information to improve the
assessment of colorless images to transfer only
chromatic information and retain the original
luminance values of the target image. This simple
technique can be successfully applied to a variety of
gray scale images and videos, provided that texture
and luminance are sufficiently distinct.
Keywords: Color map, Image Splitting, Pattern reorganization,
RGB Color space, luminance, Reference Color
Images, Object detection.
1. Introduction
Gray scale image contains pixels which are not a
RGB color pixels. Many applications convert a gray
scale image into RGB color space but fail to preserve
the original contents of a Gray Scale image. This
project provides an emphasis on noise removal, color
conversion and blur removing techniques.
Colorization is a computerized process that adds
color to a black and white print, movies and T.V
program invented by Wilson MarkLey .It was initially used in 1970 to add color to footage of moon from the
Apollo mission demand of adding color to gray scale
image such as previous black and white movies,
photos has been increasing. for e.g. in amusement
field, many movies and video clips have been
colorized by human labors and many gray scale images
have been distributed as vivid images. In other fields
such as archeology dealing with historical Gray scale
data and security dealing with gray scale images by
crime prevention camera, we can imagine easily that
colorization techniques are useful.
With respect to quality assessment, “the full-reference
still-image problem is essentially solved”[7]. This
recent, somewhat controversial statement by cocreator
of the SSIM index, sounds surprising at first. Given
that the SSIM index operates on grayscale data, color
information is obviously not required to predict these
distortions. Colors are extremely subjective and
personal. They have a prominent feature by which we
try to identify images better and improve the visual
appearance of image. Colors can be added to grayscale
images in order to increase the visual appeal of images such as to old black and white photos or movies or for
the purpose of scientific illustrations to modify it to
colorful and lively images. In addition, the information
content of some scientific images can be perceptually
enhanced with color by exploiting variations in
chromaticity as well as luminance. Since different
colors may have the same luminance value but vary in
hue or saturation, the problem of colorizing grayscale
images has no inherently “correct” solution. Due to
these ambiguities, a direct prediction of color usually
plays a large role in the colorization process. Where
the mapping of luminance values to color values is
automatic, the choice of the color map is commonly
determined by a reference image.
Here we address the color-related aspects of image
splitting. We focus on full-reference measures, which
will convert colorless image into color image [11]. Ideally, they reflect the actual visual mechanisms
responsible for image color conversion. These
mechanisms, however, are poorly understood, which
applies especially to gray scale images. While standard
methods accomplish this task by assigning pixel colors
via a global color palette, our technique empowers the
user to first select a suitable color image and then
transfer the color of this image to the graylevel image
at hand.
2. Analysis of Problem :- Changing gray scale image to color image is very
complicated. Adding direct color to a gray scale image
is not possible. One of the myths about the concept of
changing a colorless image into an color image is that
taking a color image and removing its color applying
directly to the gray scale image. To change the color of colorless image we need to modify the existing
methods. For applying color to a gray scale image,
Apurva B Parandekar et al, Int.J.Computer Technology & Applications,Vol 5 (2),735-739
IJCTA | March-April 2014 Available [email protected]
735
ISSN:2229-6093
segmentation is used first but due to improperness in
its separation resultant image quality may loss.
According to referred methods, predicting and then
direct application of color on the colorless image is
one of the main problems which reduced the overall
effectiveness of the entire work. This drawback makes
the overall system rigidless so that, the prediction
about the color of image should not match every time. The changes in source image and resultant image are
not completely verified depending upon its image
difference mapping. Difference was calculated by
considering few parameters and reference image in the
previous case and then that reference image are
converted into the color. The mapping methods used in
current scenario are not satisfactory.
3. Proposed Methodology 3.1Objectives Adding color to a gray scale image directly is not
possible. Admittedly, the process of colorizing a
grayscale image certainly seems not straight forward
enough, in that it probably involves various methods to
apply color onto the colorless image. This technique is
entirely different; it is an adaptive system, which
emphasis on a grayscale image and converting them
into color image using image splitting.
Automatic selection of color in a particular grayscale
image makes system more impactful and obtained
resultant image enhanced the scale of colorization.
Purpose of this Paper is to produce with the new
approach to work on the way to get the solution of problem in hand.
In previous adapted methods the difference mapping
is done partially. Here we proposed that the method
used in predicting maps will be uniform and having
effective mapping techniques between the gray scale
image and obtained color image. In this proposed
system we will map the entire source and destination
images for better results..
3.2 Algorithm
1. Select colorless gray scale image.
2. Split an Image into multiple segments.
3. Locate pattern for each segment.
4. If pattern located successfully, then goto step5 else select default color reference image.
5. Insert reference image color to gray scale object.
6. Join all converted color objects.
7. Stop.
Figure3.2.1 DFD(Proposed System)
Figure 3.2.2 Proposed System Architecture
3.2.1 Select Gray Scale Image In Proposed method, main objective is to convert
gray scale image to color RGB Image with respect to
its content. Input image may be of any type like
.jpg,bmp,png,tiff…. etc
3.2.2 Split an Image Split an image object into multiple objects so that it
can be properly clusterise with respect to its content.
Figure 3.2.1Input Image
Apurva B Parandekar et al, Int.J.Computer Technology & Applications,Vol 5 (2),735-739
IJCTA | March-April 2014 Available [email protected]
736
ISSN:2229-6093
Figure 3.2.2 Split Image Objects
An entire image object can be represented with an
equation
𝐼𝑚 = 𝐼𝑗 + 𝐼𝑗 + 1 + ⋯+ 𝐼𝑚 𝑑𝑤𝑚
𝑗 =1 eq……3.1
Where Im=single object vertically split.
Ij=Veridical split sub-object of object split horizontal
dw=individual object width of I.
+ =Horizontal Join
An image can be constructed with an equation
𝐼𝑚𝑎𝑔𝑒 = 𝐼𝑚 𝐼𝑚 + 1 𝐼15 𝑑ℎ15
𝑚 =1 eq…….3.2
Where
dh=Individual object Im height.
||=Vertical Join
3.2.3 Select Reference Image
Figure 3.2.3.1 DFD for Pattern Matching
In this project we create a system that is useful to find
the object that is present inside the image. In this we
first take a image as an input for which we have to find
the object. After selecting the input image we have to
focus on the target image. After getting both input
image and target image we compare the size of both
the image. If the size of input image is greater than
target image then we proceed with our system
otherwise we are going to focus on the input image. If
the above criteria get satisfied then we create the sub
matrixes of both the images for example we create 3 X
3 matrixes. After creating the sub matrixes we
compare both the sub matrixes of both the image. This
Matrix can be created with the help of pixels present in
both the images. If matching found then we concludes
that the given object is found in the other image. The object is shown as an output by creating red boxes on
that object.
The output object image is displayed and the co
ordinates of that object that is the height and width
from top and bottom in te form of coordinates is shown
with the help of im tool.
3.2.4 Fill Reference Color Image to Gray Scale
Object Process of gray scale to GRB color conversion works
as follows
Gray Scale Object Color Object
Figure 3.2.4 Basic Pixel Representation
In a proposed methodology, we replace color
content of gray scale image object with color content
of reference color image
Select a Gray scale image of any type(i.e. JPEG,
BMP, PNG etc).If an image is of large dimensions, we
must need to resize it into a dimension of 200 X
200.The reason behind this is that, number of pixels
are directly proportional to time required for color
conversion.
Pi ∝ Tieq……..3.4 Where Pi=Number of pixels of input gray scale.
Ti=Time required for color conversion.
The reference image should be a color RGB image.
The reason behind is that, we are creating a dummy
image with color contents of reference color image.
3.2.5 Luminance Comparison Luminance of an image is a factor that preserves the
originality of an image. The luminance factor can be
calculated as
Apurva B Parandekar et al, Int.J.Computer Technology & Applications,Vol 5 (2),735-739
IJCTA | March-April 2014 Available [email protected]
737
ISSN:2229-6093
Lfr = Li – Lf eq…………….3.5
Where
Li= Pi Pr + Pg +Pb
r +
Pr + Pg +Pb
g +
𝑛
𝑖=0
Pr + Pg +Pb
b eq………3.5
Lf = Pf Pfr + Pfg + Pfb
fr +
Pfr + Pfg + Pfb
fg
𝑛
𝑖=0
+ Pfr + Pfg + Pfb
fb
eq………3.6
Li=Gray scale factor of original Gray scale image.
Lf= RGB factor of original reference image.
Pr=Red component decimal value of input Gray scale.
Pg=Green component decimal value of input Gray scale.
Pb=Blue component decimal value of input Gray scale.
Pfr=Red component decimal value of reference image.
Pfg=Green component decimal value of reference
image.
Pfb=Blue component decimal value of reference image.
If Lfr ≤ 0 ≤ 10
it means both the pixels have a matching color
parameters else not.
3.2.6 Generating Dummy image A dummy image has a great impact on accurate
colorization of gray scale image. Dummy image
contains only color components of reference image. A
dummy image created with following steps:
1. Segment reference image according to color
components. Reference image can be segmented
using
𝑃𝑖 Pr + Pg +Pb
r+g+b
𝑃𝑦𝑥𝑖=0
𝑃𝑥𝑦
0
𝑛
1 eq…….3.7
Where
n = No. Of segments
Pxy = Total no. of pixels Pyx = Pixels at position Y, X
2. Add a segment generated as per the Eq 3.7 into
dummy image. Put the contents of input Gray scale
image into dummy image
It is observed that generated color image may have
contents which are not properly synchronized with
respect to color. This project has an accuracy 80 to
90% for correct image segmentations and for dummy
image generation. Usually contents in an image are represented with pixels value either 0 or 1.This
technique works on same concept. To have a better
accuracy, it should be necessary that luminance factor
calculations, image segmentations should be correctly
done.
4. Experiment Result
Input Image Recovered Color
Image
Sr.
No
Im
ag
e
Na
me
Size
Mea
n
Inte
nsit
y
Ent
rop
y
Col
or
Ima
ge
Size
Mea
n
Inte
nsit
y
Ent
rop
y
1 1.j
pg
100
x10
0
0.5 17.3
5
100
x10
0
0.43 17.6
2
2 2.j
pg
150
x15
0
0.4 18.2
4
150
x15
0
0.47
8
18.2
3
3 3.j
pg
200
x20
0
0.5 17.3
52
200
x20
0
0.5 17.9
87
4 4.j
pg
250
x250
0.6 18.2
34
250
x250
0.62
34
18.4
64
Table 4.1 Input image vs Recovered color image
Table 4.2 Pixel change result
Sr.
No
Inp
ut
Im
age
Siz
e
No of
Refer
ence
Image
s
Ref.Im
ages
Locate
d
Time for
Recover
y(Sec)
Accu
racy
%
1 100
x10
0
10 8 58 83
2 150
x150
10 8 85 90
Apurva B Parandekar et al, Int.J.Computer Technology & Applications,Vol 5 (2),735-739
IJCTA | March-April 2014 Available [email protected]
738
ISSN:2229-6093
3 200
x
200
10 9 95 95
4 250
x250
10 8 93 98
Table 4.3 Time & accuracy result
5. Conclusion In this paper,fast simple technique is used to
colorize a gray scale image to a color image using
reference image.Original image is pre-processed to
illuminate noise and then colorisation process is
done.By using this technique a large number of Gray
scale images are converted into color images with very
less effort.
References [1] A. K. Jai, Fundamentals of Digital Image
Processing, Prentice-Hall, 1989.
[2] “Colorization for Monochrome Image with
texture”, in coloring Image Conference 2005, pp 245-
250.
[3] “Converting Gray-Scale Image to Color Image” in
Proceedings of SPIT-IEEE Colloquium and
International Conference, Mumbai, India, Vol. 1, 189.
[4] Gonzalez R.C., Woods R.E., Digital Image Processing, Addison Wesley, 2002.
[5] K. S. Sim, C. P. Tso, and Y. Y. Tan, "Recursive
sub-image histogram equalization applied to gray scale
images”", Pattern Recognition Letters, 28(10), pp.
1209-1221, 2007.
[6] Pratt W.K., Digital image processing, A Wiley
Interscience Publication, 1991.
[7] Scott E Umbaugh, Computer vision and i mage
[8]MahmoudK.Quweider“AdaptivePseudocoloring of
Medical Images Using Dynamic Optimal Partitioning
and Space-FillingCurves” Biomedical Engineering and
Informatics, 2009. BMEI '09. 2nd International
Conference, PP. 1-6,17-19 Oct. 2009.
[9] Ye Ji, Yan Chen, “ Rendering Grayscale Image
using Color Feature” IEEE Int. Conf. on Machine
Learning and Cybernetics, Vol.5, pp. 3017-3021,
Kunming, China, 2008 [10] C.W.Kok, Y.Hui, T.Q.Nguyen, “Medical Image
Pseudo Coloring by Wavelet Fusion” 18th Annual
International Conference of theIEEE Engineering in
Medicine and Biology Society. Amsterdam,1996.
[11] GovindHaldankar, AtulTikare and JayprabhaPatil,
“Converting Gray-Scale Image to Color Image”
Proceedings of SPIT-IEEEColloquium and
International Conference, Vol. 1, pp. 189-
192,Mumbai, India, June 2011.
[12] Cheng-Hsiung Hsieh, Chih-Ming Lin, Fung-Jung
Chang, “Pseudo coloring with Histogram
Interpolation” Ninth InternationalConference on
Hybrid Intelligent Systems 2009.
[13]TomihisaWelsh,MichaelAshikhmin and Klaus
Mueller, “Transferring color to grayscale images”,
Journal ACM Transactions on Graphics (TOG) -
Proceedings of ACM SIGGRAPH 2002TOG, Volume
21 Issue 3, PP. 277-280 , July 2002.
[14]Rujuta R Mahambare “CONVERTING GRAY-
SCALE IMAGE TOCOLOR IMAGE”, IJCITB ISSN:
2278-7593, Volume-1, Issue-4 IEEE & IEEE Computational Intelligence Society,PP.142-146, April-
June 2013 Issue.
[15]Dr.AhlamFadhilMahmoodAbdulkreem
Mohammad Salih,“Implementation of Multiplier less
Architectures for Color Space Conversions on
FPGA”,IEEE Transactions on Consume Electronics,
Volume 53, Number 4, PP1490-1493,November 2004.
[16] Stephen J. Chapman, Second Edition Matlab
Programming for Engineering,[2002].
[17] Ingmar Lissner, Jens Preiss,” Image-Difference
Prediction:From Grayscale to color”, ieee transactions
on image processing, vol. 22, no. 2, pp.435-446 feb
2013.
Author Information Prof A.B.Parandekar: Completed B.E. (IT) from Sant
Gadge Baba Amravati University, Amravati. Currently
pursuingME in faculty of Engineering & Technology,
and her area of interest is Digital image processing.
Prof. Shital. S. Dhande: She is Associate Professor at
Sipna College of Engineering & Technology,
Amravati. Completed her B.E. (CSE), M.E. (CSE)
from Sant Gadge Baba ,Amravati University, Amravati. Currently pursuing PHD in faculty of
Engineering & Technology in the area of OODatabase
Management System from Sant Gadge Baba Amravati
University, Amravati (MH), India. She has published
many papers at National as well as International level.
She is a member of ISTE, IE, and IETE India.
Apurva B Parandekar et al, Int.J.Computer Technology & Applications,Vol 5 (2),735-739
IJCTA | March-April 2014 Available [email protected]
739
ISSN:2229-6093
![RGB-Guided Hyperspectral Image Upsampling...age and a high-resolution RGB (or gray scale) image to re-construct a high-resolution hyperspectral image as demon-strated in [12, 2, 9]](https://img.dokumen.tips/doc/110x75/6014d16668935b1b1d799219/rgb-guided-hyperspectral-image-upsampling-age-and-a-high-resolution-rgb-or.jpg)
