PSEUDO COLOR IMAGE PROCESSING

A Minor Project Report

Submitted in Partial Fulfillment of the Requirementsfor the Degree of

Bachelor OF TECHNOLOGY

IN

ELECTRONICS & COMMUNICATION

ENGINEERING

ByJaydip R. Fadadu 08BEC024

Kuldip R. Gor 08BEC030

Under the Guidance ofProf. Tanish H. Zaveri

Department of Electrical EngineeringElectronics & Communication Engineering

ProgramInstitute of Technology, Nirma University

Ahmedabad-382481November 2011

CERTIFICATE

This is to certify that the Minor Project Report entitled “ Pseudo Color Image

Processing “ submitted by Jaydip Fadadu (Roll No. 08BEC024) and Kuldip

Gor ( Roll No. 08BEC030) as the partial fulfillment of the requirements for the

award of the degree of Bachelor of Technology in Electronics &

Communication Engineering, Institute of Technology, Nirma University is the

record of work carried out by his/her under my supervision and guidance. The

work submitted in our opinion has reached a level required for being accepted

for the examination.

Date:

Prof. Tanish H. Zaveri

Project Guide

Prof. A. S. Ranade

HOD (Electrical Engineering)

Nirma University, Ahmedabad

ACKNOWLEDGEMENT

It gives us great pleasure in expressing thanks and profound gratitude to we like to

give our special thanks to our Faculty Guide, Prof Tanish Zaveri, Professor, Department of

Electronics & Communication Engineering, Institute of Technology, Nirma University,

Ahmedabad for his valuable guidance and continual encouragement throughout the Minor

Project. We are heartily thankful to him for his regular suggestions and the clarity of the

concepts of the topic that helped us a lot during this project.

We are also thankful to Prof A. S. Ranade, HOD, Department of Electronics &

Communication Engineering, Institute of Technology, Nirma University, Ahmedabad for his

continual kind words of encouragement and motivation throughout the Minor Project. We are

also thankful to Dr K Kotecha, Director, Institute of Technology for his kind support in all

respect during our work.

We are thankful to all faculty members of Department of Electronics &

Communication Engineering, Institute of Technology, Nirma University, Ahmedabad for

their special attention and suggestions towards the project work.

The friends, who always bear and motivate us throughout this course, we are thankful

to them.

Mr. Jaydip Fadadu Mr. Kuldip Gor 08BEC024 08BEC030

ABSTRACT

Human eye can distinguish only a limited number of gray scale value but can distinguish

between thousands of color. So it is clear that human can extract more amount of information

from the colored image than that of the gray image. So pseudo coloring is very useful in

improving the visibility of an image. Certain application like buggage scanning at airport or

in medical field or in night vision camera works in the band of X ray and infrared so the

images produced by this techniques always gives gray image. At the same time the proper

inspection of this all images is very crucial and critical. So coloring of those images is very

important. Moreover color should be applied in such a way that it improves the visibility of

image in optimal way for that particular application. The project discusses various techniques

of pseudo coloring for images of above mentioned applications.

Index

Chapter No.

Title Page No.

Acknowledgement i

Abstract Ii

Index iii

List of Figures iv

List of Tables v

Nomenclature vi

1 Introduction 1

1.1 Introduction 1

1.2 Necessity of project 1

1.3 Objective of project 2

1.4 Contents of the report 3

2 Classification of Pseudo Coloring Technique 4

3 Pseudo Coloring of Night Vision Images 73.1 False Color Fusion and Color Enhancement 8

3.2 Color Based Clustering Using Hill Climbing 83.3 Cluster Recognization 9

3.4 Color Transferring 9

4 Pseudo Coloring of x-ray Images of Medical & Luggage scanning @ Air-port

11

4.1 Simple Process of Pseudo Coloring 11

4.2 Various Coloring Techniques 12

4.3 Proposed Method based on Look Up Table designed from Warm Color Scale

18

5 Evaluation Parameters 23

Conclusions 24

References 25

LIST OF FIGURES

Fig. No. Title Page

No.

1 Block Diagram Of Color Transfer Method 7

2 Results of Night Vision Color Image 10

3 Simple Block Diagram of Pseudo coloring 11

4 The HOT color Scale 12

5 R,G and B v/s I for HOT coloring 13

6 The JET color Scale 13

7 R,G and B v/s I for JET coloring 13

8 R,G and B v/s I for HSI coloring 14

9 The HSI color Scale based on Concave Part(solid part of Fig. 8) 14

10 The HSI color Scale based on Convex Part(solid part of Fig. 8) 14

11 The RAINBOW color Scale 15

12 R,G and B v/s I for RAINBOW coloring 15

13 Simulation Results of various Coloring Methods 16

14 Detailed Block Diagram of Proposed Method based on Look Up

Table designed from Warm Color Scale

18

15 The WARM color scale 20

16 R,G and B v/s I for WARM coloring 20

17 Simulation Results of Proposed method based on Look up table

created as per warm color scale

21

LIST OF TABLES

1 Entropy and Colorfullness Mertic Comparision 23

NOMENCLATURE

Vis Visible Image

IR Infrared Image

μk , s Mean kth cluster in source images

σ k ,s Standard deviation of kth cluster in source images

μn , t Mean of nth target image

σ n ,t Standard deviation of nth target image

Q Similarity matrix

RGB Red-Green-Blue Color space

HSI Hue-Saturation-Intsnsity Color space

Chapter 1

Introduction

1.1 Introduction

Pseudo-color image processing assigns color to grayscale images. This is useful because the

human eye can distinguish between millions of colors but relatively few shades of gray.

Pseudo-coloring has many applications on images from devices capturing light outside the

visible spectrum, for example, infrared and X-rays. A pseudo-color image is derived from a

grey scale image by mapping each pixel value to a color according to a table or function. The

table or the function is decided in such a way that it enhances the visibility optimally for a

particular application. To achieve that objective various color scales are defined in the

literature. The color scales are defined in different color spaces like RGB or HSI etc. before

performing the actual color assigning the gray scale image is enhanced by performing certain

operations on it. The project discusses the various techniques of enhancing the image and

performing pseudo coloring on it to enhance the visibility.

1.2 Necessity Of the Project

Achieving higher threat detection rates during inspection of X-ray in luggage scans and

medical field is a pressing and sought after goal for airport and airplane security personnel as

well as doctors. Because of the complexities in knowing the content of each individual bag

and the increasingly sophisticated methods adopted by terrorists in concealing threat objects,

X-ray luggage scans obtained directly from major luggage inspection machines still do not

reveal 100% of objects of interest, especially low density threats. So in both the cases if the

gray density value of the threat is very low then it may easily go unchecked. Various types of

grayscale enhancements are done to improve the detection but It was shown, through screener

evaluation studies that an improvement of 57% in detection rates accrued after enhancement

as compared to the inspection results from the raw data. Furthermore, since it is known that

human beings can only discern a few dozen gray level values while they can distinguish

thousands of colors, the use of color for human interpretation could only improve the number

of objects that can be distinguished. In addition, color adds vivacity to images, which in turn

decreases boredom and improves the attention span of screeners. Moreover Modern night

vision camera systems are used for military and law enforcement applications to design

intelligent surveillance systems for security. These systems are designed to provide enhanced

image with better perceptual quality in adverse environmental conditions. The most common

night-time imagery systems capture images in two spectral bands, near infrared (NIR) and

visual, thus providing complementary information of the observed scene which enables the

observer to perceive more complete picture of the scene with a larger degree of situational

awareness. A fused image combines all the salient information from the source images which

is more suitable for human/ machine perception. Image fusion is an effective way of reducing

the volume of information while at the same time it preserves all the useful information from

the source images. The rapid development in the technology of night vision (NV) systems has

led to a growing interest in the natural color display of night vision imagery. As human visual

system is more sensitive to color information, efficient color transfer methods are required to

enhance color image which has several benefits over gray image. The color transfer methods

improve feature contrast, allowing better scene recognition and object detection which is

useful in surveillance, reconnaissance, and security applications.

1.3 Objective Of the Project

Given the fact that the human eye is more sensitive to some parts of the visible spectrum of

light than to others and that the brain may interpret color patterns differently, the

interpretation of results produced by visualization techniques depends crucially on the color

mapping applied. In an effort to address the relatively new problem of visualizing low-

density threat items in x-ray images, while incorporating considerations of the perceptual and

cognitive characteristics of the human operator, a set of RGB- and HSI- based color

transforms were designed and applied to single energy X-ray luggage scans. The analysis

showed that color coding resulted in a large improvement in the detection rates of threat

objects in luggage and in an increased screener’s visual and mental alertness and attention

retention.

In night vision color images it is very important that coloring should be natural. It mean colr

should be applied in such a way that after coloring the scene seems natural at the same time it

should be efficient in identifying the human presence. So the same method cannot be applied

to both night vision infrared image and X-ray image. So two very different methods are

proposed in the project for pseudo coloring of both different types of images.

1.4 Content Of The Report

The report starts with the basic introduction of the pseudo coloring. Chapter 1 discusses the

basic need of applying pseudo color to gray scale image and its application in night vision

camera and X-ray images. Chapter 2 discusses the various types of coloring techniques that

are used in different fields. Then proceeding chapter discusses the pseudo coloring of night

vision images using image fusion and how to apply the natural color to NIR images. The next

chapter discusses the pseudo coloring of X ray images.

Chapter 2

Classification Of Pseudo Coloring TechniqueBased on available literature, pseudo coloring techniques can generally be grouped into five

main categories:

1) Spectrum-based maps, where color scales are designed by having the hue sequence

range from violet, via indigo, blue, green, yellow, and orange, to red, following the color

order of the visible spectrum. Since the human visual system has different sensitivities to

different wavelengths, researchers such as Clarke and Leonard indicated that spectrum-based

color scales were not perceived to possess a color order that corresponds to the natural order

of grayscale in the image.

2) Naturally ordered maps, where a number of researchers attempted to define a

particular path traversing the RGB color space under certain predefined constraints. The

heated-object scale is achieved by bringing the RGB intensities up in the order of red, green,

and blue, and limiting the path to 60◦ clockwise from the red axis. This selection is based on

the claim that natural color scales seem to be produced when the intensities of the three

primary colors rise monotonically with the same order of magnitude throughout the entire

scale. To construct a natural color scale, Lehmann et al defined a spiral-like scale in the RGB

model, keeping the original brightness progression of grayscale images. Specifically

speaking, their color scale follows a spiral-like path along the diagonal of the RGB model.

The authors formularized such a scale to allow the determination of the resulting number of

colors, and demonstrated their scale’s effectiveness by applying it to medical X-ray images.

They also pointed out that better results could be obtained if other color models were used.

The use of HSI was suggested as future work based on the fact that the lightness component

is directly represented by one of the axes in HSI.

3) Uniformly varying maps, where several studies focused on constructing a uniform

color scale where adjacent colors are equally spaced in terms of just noticeable differences

(JNDs) and maintain a natural order along this color scale. Levkowitz and Herman’s research

provided a scale with the maximum uniform resolution. The authors were hoping that their

optimal color scale outperforms the grayscale, but evaluation results did not confirm that, at

least for their particular application. They presented several reasons that might have caused

the unexpected results. One particular reason was that they used the CIELUV model to adjust

the final colors which might not have been appropriate to model the perceived uniformity.

Another reason was that the perceived change in color due to its surrounding was not taken

into consideration. Shi et al designed a uniform color scale by visually setting up its color

series to run from inherently dark colors to inherently light colors; i.e., from black through

blue, magenta, red, yellow to white, then further adjusting the colors to make them equally

spaced. The color scales were evaluated by comparing them to the grayscale. The authors

indicated that the contrast sensitivity has been improved after applying their uniform scale,

but they failed to demonstrate any significant outcome.

4) Function-based maps, where researchers utilized common mathematical functions

such as the sine function to construct desired mappings or color scales. Gonzalez and Woods

described an approach where three independent mathematical transforms were performed on

the gray level data, and the three output images fed into the R, G, and B channels to produce

a specific color mapping.

5) Refernce Natural Images Database Technique

There are various approaches available in literature, which are broadly divided into three

different categories: statistical approach, region-based approach, pattern recognition based

approach. However from the recent literature, we found that combinations of these

approaches are used to improve the quality of resultant image. Yang et al. in have proposed

region-based approach for color transfer in night vision image sequences. The method uses

support vector machines for region recognition among a set of natural color database. In case

of specific application of color transfer in night vision FLIR (Forward Looking Infrared)

images based on texture pattern recognition for color transfer images, Sun and Zhao proposed

a method for automatic natural color mapping for FLIR. A local-coloring method utilizing

image analysis and fusion was introduced by Zheng and Essock in, which render the NV

image segment-by-segment by taking advantage of image segmentation, pattern recognition,

histogram matching and image fusion. Recently, A. Toet proposed a fast color mapping

method in which the mapping optimizes the match between the multi-band image and the

reference natural image, and yields a night vision image with a natural daytime color

appearance. This lookup-table based mapping procedure is simple and provides object color

constancy. However the scene matching between source and target is performed manually

which may yield less naturalistic results for images containing regions that differ significantly

in their content. Gang and Huang presented a multi-scale color image fusion using contourlet

transform and expectation maximization (EM) where the color transfer is implemented in

YUV color space. A new linear color space ICbCr was proposed by Xu and Li in especially

for multiband night vision imagery to transfer the color distribution of the target image to the

source NV images.

Chapter 3

Pseudo Coloring Of Night Vision Images In the color transfer method color based clustering is applied on color map in LAB color

space and cluster recognition is performed based on color similarity metric. The block

diagram of the proposed color transfer method is shown in Figure 1. As shown in Figure 1,

false colored nightvision image is obtained by assigning linear combinations of visible and IR

source images to the RGB channels. The false colored NV image is enhanced by

decorrelation stretch and contrast stretch. A colormap of the enhanced false colored

Fig. 1 Block Diagram Of Color Transfer Method

NV image is obtained. Color based clustering is performed on the colormap in the LAB color

space using hill climbing algorithm. The association between each cluster and a natural color

image in the target color look-up table is carried out automatically utilizing a nearest

neighbour criteria based on a color similarity metric. The color components in each index

within a cluster of the colormap are modified by statistics matching with the corresponding

natural color image. The natural colored nightvision image is produced from the new

colormap. Finally, the natural colored NV image is transformed to HSV color space and the

enhanced gray image is substituted in the “value (V)” component to generate the final output

of the proposed method. The subsequent subsections of this section describe the detailed

explanation of the major steps of method [3].

3.1 False Color Fusion and Color Enhancement

The false color fused RGB image can be represented by the following equations:

R(m; n) = 12

(Vis(m; n) + IR(m; n))

G(m; n) = IR(m; n) B(m; n) = Vis(m; n) - IR(m; n)

The false color fused image so formed has intensity variations similar to visible and IR

source images. In order to achieve better seperation in color based clustering we perform

decorrelation stretch for color enhancement and linear contrast stretch for intensity

enhancement. Decorrelation stretch as described in increases color seperation across highly

correlated channels while keeping the band variance same. Decorrelation stretch makes many

features easier to observe which were not clearly visible in the original image.

3.2 Color based Clustering using Hill Climbing

Color based clustering is performed on the LAB colormap using the hill climbing algorithm.

A color based image segmentation method using hill climbing algorithm proposed by Ohashi

et al. is utilized for colormap clustering in the proposed method. The number of clusters

required for proper classification of colormap are automatically determined by the hill

climbing algorithm. A three-dimensional histogram is computed in the LAB color space. The

hill climbing algorithm can be seen as a search window being run across the space of the 3D

LAB histogram to find the largest bin within that window. Each bin in the color histogram

has 3d - 1 = 26 neighbours where d = 3 is the number of dimensions of the feature space. The

number of peaks found indicate the value of K and the value of those bins form the initial

seeds for the K-means segmentation. Thus, local maxima are found for clusters in the 3D

color histogram of the colormap in the LAB color space. The entries of the colormap are then

associated with the detected local maxima to generate several coherent clusters in the LAB

colormap.

3.3 Cluster Recognition

The target color look-up table is created as follows. Each image from the natural color target

image database is firsth smoothed by separately applying low pass filter in each RGB

channels. This operation reduces the number of colors and enables the extraction of dominant

colors from the natural image. The smoothed image is then transformed into LAB color space

and first order statistics, mean µ and standard deviation σ , are computed for each band. A

nearest neighbour criteria is used for automatic association of a cluster of colormap with a

unique natural color image in the target color look-up table. The similarity metric

Where

Is used for the measure of the distance between Kth cluster and nth image.

3.4 Color Transfer

Color transfer is performed cluster-by-cluster by the standard statistics matching method

proposed by Toet. Each index in LAB colormap is first checked as to which cluster does it

belong and then the statistics of natural color target image associated with that cluster is

transferred to the LAB values of the index in colormap. Thus a new LAB colormap is

obtained. The equations for color transfer on each index in the colormap are defined as

follows:

Kth cluster is associated with nth natural image. μk , s and σ k ,s indicates the mean and standard

deviation of kth cluster in source images. μn , t and σ n ,t indicates the mean and standard

deviation of nth target image.

Fig. 2 Results of Night Vision Color Image

Chapter 4

Pseudo coloring on X-ray images of Medical and

Luggage scanning @ AirportIn this project we mainly focus on two major areas:

1. Medical

Pseudo coloring is done to enhance the visibility of the fracture or any disease which

is not clearly visible by naked eyes in x-ray image.

2. Luggage Scanning @ Air-port

Pseudo coloring is done to increase the detect ability of the low density weapons

which are not clearly detectable in simple x-ray scanning.

4.1 Simple Process of Pseudo Coloring

To develop enhanced color image from x-ray image, processing is done on x-ray image. A

simple block diagram of that process is shown in figure 3. Few steps are followed before

coloring to increase the visibility.

Fig. 3 Simple Block Diagram of Pseudo coloring

X-Ray Image

Contrast Stretch

Salt & Pepper Noise Removal

Color Conversion

Enhanced Color Image

A. X-ray Image:

It is the simple black and white image taken by x-ray camera. Each pixel of the image

has information i.e. intensity of which value varies from 0 to 255. Processing is done

on this image.

B. Contrast stretch

This process increases the difference between maximum and minimum intensity. In

most of the methods simple linear stretch is applied.

C. Salt & Pepper Noise Removal

After contrast stretching, Salt and pepper noise is removed. It is done by applying

various filters. In most of the cases median filter is applied. At the end of this block

we get enhanced gray image with more information regarding weapons in case of

luggage scanning or fractures in case of medical images.

D. Color Conversion

It is the process of converting enhanced gray scale image into color image. Various

methods for color conversion are available [1][2][5]. We have studied 4 basic

methods:

i. Hot

ii. Jet

iii. HSI

iv. Rainbow

All the methods are further explained in detail.

4.2 Various Coloring Techniques

1. HOT

It is RGB based linear color map. “Hot” changes smoothly from black, through shades of red, orange, and yellow to white as shown in fig. 4.

Fig.4 The HOT color Scale

In fig. 3 RGB values versus gray value is shown for HOT coloring. That plot shows

the variation of R,G and B with respect to I.

Fig. 5 R,G and B v/s I for HOT coloring

2. JET

It is RGB based linear color map. It ranges from Blue to Red, passing through Cyan, Yellow and Orange as shown in fig.4. In fig. 5 RGB values versus gray value is shown for JET coloring. This color map can be obtained by converting this plot into simple mathematical formulas.

Fig. 6 The JET color scale

Fig. 7 R,G and B v/s I for JET coloring

3. HSI

It is Histogram based non-linear color mapping. The colors of the various components in the scene are assigned based on the values of the raw image. Pixel ranges are selected from the data’s histogram and automatically given certain colors. For example, four gray-level regions were created, the chances of the low density threat being present would be greatest in the first two regions. Blue will be used as background and other easy-to-remember basic colors like red and green are applied to the other pixels in each bin. The output image would have four hues, which vary as a function of the gray intensity value of each pixel.

Fig. 8 H,S and I v/s gray for HSI based coloring

Fig. 9 HSI color scale based on concave part(solid part of fig. 8)

Fig. 10 HSI color scale based on convex part(solid part of fig. 8)

4. Rainbow

It can be considered as a special case of Sine/Cosine transform. 3 color transfer

functions are used for rainbow map. All the functions are periodic in the sense that

they get peak in particular color interval. Rainbow based color map and RGB’s

relation with I are shown in following figures.

Fig. 11 The Rainbow color scale

Fig. 12 R,G and B v/s I for Rainbow color map

Fig. 13 Simulation Results of various Coloring Methods

4.3 Proposed Method based on Look Up Table designed

from Warm Color Scale

This method is specially designed for pseudo coloring in x-ray images for weapon

detection and medical. In fig. 14 a detailed block diagram is shown.

X-Ray Image

Contrast Stretch Using Intensity Adjust

Warm Color Map

Enhanced Color Image

Adaptive Histogram Equalization

Noise Removal Using 2D-Median Filter

Look Up Table

Enhanced Gray Scale Image

Color Conversion Using Look Up Table

Fig. 14 Detailed Block Diagram of Proposed Method based on Look Up Table designed

from Warm Color Scale

Initially Look Up Table is created based on WARM color scale. Warm color scale is

explained in [1][2][5].

A simple x-ray image is passed through various blocks to enhance the information

regarding fractures/cracks or weapons in case of medical or weapon detection @

airport respectively.

Pseudo coloring on this enhanced gray image is done based on the Look Up Table

prepared earlier as per warm color scale.

Explanation of the above block diagram:

[1]. X-Ray Image

It is the simple black and white image taken by x-ray camera. It may be the image of

any body part taken for medical use or may be image of weapon detection @ air-port.

Each pixel of the image has information i.e. intensity of which value varies from 0 to

255. Processing is done on this image.

[2]. Adaptive Histogram Equalization

It is the process of enhancing the contrast of images by transforming the values in the

intensity image I. Unlike simple histogram equalization it operates on small data

regions (tiles), rather than the entire image. Each tile's contrast is enhanced, so that the

histogram of the output region approximately matches the specified histogram. The

neighboring tiles are then combined using bilinear interpolation in order to eliminate

artificially induced boundaries. The contrast, especially in homogeneous areas, can

be limited in order to avoid amplifying the noise which might be present in the image.

In this proposed method tile size is taken as 16 x 16.

[3]. Contrast Stretch using Intensity Adjust

Enhanced Color Image

By this block linear intensity contrast stretch is applied. It increases the difference

between maximum and minimum intensity. MATLAB inbuilt function imadjust is

used for this function.

[4]. Noise Removal Using 2D-Median Filter.

At the end of the contrast stretching we get the image which may have few unwanted

noise dots. It should be removed for false detection. This can be removed by filter. In

this proposed method 2 dimensional median filter is used. It is the filter which takes

the median of given square block. Here we have taken 3 x 3 block.

[5]. Enhanced Gray Scale Image

This is the final gray scale image i.e. enhanced image having more information than

original one. Now the coloring is done on this image.

[6]. Warm color scale

This is non-liner color scale. It varies from Dark Blue to Light Yellow through Magenta and Orange as shown in fig. 15. The distances of adjacent colors on this scale are perceivably equal. A 256-step scale as seen in Fig. 11 was developed. For any intensity I(i) and I(i+1) the correspondence (R, G, B)(i) < (R, G, B)(i+1). This law is followed throughout the color scale. Respective relation of R,G and B with I is shown in fig. 16.

Fig. 15 The WARM Color Scale

Fig.16 R,G and B v/s I for WARM coloring

[7]. Look Up Table

It is a simple table which gives the corresponding R, G and B values for given

intensity I. It is designed based on the Warm Color Scale which is already explained

earlier.

[8]. Color Conversion using Look Up Table

Color Conversion i.e. I to (R,G,B) is done based on the look up table created earlier.

The coloring is done on enhanced gray scale image. It is just a simple assignment of

(R,G,B) as per the value of intensity of that pixel.

[9]. Enhanced Color Image

This is the final color image having more, clear information regarding fracture or

cracks in medical images or weapons in luggage scanning @ airport.

Fig. 17 Simulation Results of Proposed method based on Look up table created as per

warm color scale

Chapter 5

Evaluation Parameters for ColoringThe quality assessment of different image fusion schemes for X-ray images is traditionally carried out by subjective evaluations. The subjective evaluation is influenced by individual human perception. In recent literature [4] objective evaluation parameters are proposed. Here we have considered non reference based evaluation parameters; entropy and colorfulness metric. Entropy is used to measure the information content of an image. The entropy of a grayscale image is:

where G is the number of gray levels in the image’s histogram (which can be 255 for a typical 8-bit image) and p(i) is the normalized frequency of occurrence for each gray level, i.e., the histogram of the image. The entropy of colored image is computed for each band in RGB color space and average of the entropy of the three bands is considered for evaluation. Colorfulness metric is an efficient metric for calculating colorfulness of images and it is described in [4]. Larger the color variations in the image, higher is the colorfulness metric. The proposed algorithm is compared with the standard statistics matching method proposed by Toet. The simulation results of proposed algorithm are shown in Figure 17 and the simulation results of other coloring techniques are shown in figure 13. It is observed that proposed method provides more informative appearance compared to other method [1] and [5] as shown in Figure 13 and Figure 17. Hence the cracks in medical image and weapons in luggage scanning the scene can be easily distinguished. Their comparison with other techniques using parameters like entropy and colorfullness metric are shown in table 1.

  ENTROPY

NO HOT JET HIS RAINBOW PROPOSED1 0.9734 3.607 5.4859 6.4439 5.772

2 0.67643.514

9 5.5525 6.4578 5.5401

3 0.73893.623

1 5.5841 6.5618 5.5839

4 0.76433.860

5 5.5473 6.443 5.5479

5 0.63583.524

4 5.7071 6.9876 5.6924

6 1.51713.666

1 5.4594 5.9877 5.7758

  COLORFULLNESS METRIC

  HOT JET HIS RAINBOW PROPOSED

1 165.820.893

7 0.989 0.3512 76.3414

2 159.180.894

7 0.9973 0.3512 73.7114

3 171.640.924

1 0.8892 0.3512 73.7709

4 185.310.939

6 0.9736 0.3512 81.5495 156.77 0.811 0.7456 0.3512 78.31356 210.2 1.073 0.8602 0.3512 87.2228

Table 1. Entropy and Colorfullness Mertic Comparision

CONCLUSION

Thus in this report we presented efficient method for pseudo coloring of grayscale images.

Here we discussed the pseudo coloring of two different types of images. The first is Night

vision infrared image and second is X ray images. Night vision images are used in army and

nevy applications while the X ray images are used at air ports as well as in medical. Thus we

can see that it is very crucial that the image should be visibly best as possible. So that

personnel can extract information as much as possible. So we apply color to those images in

this project. We can see that the colored images are far better to analyze than the gray images.

Moreover same method cannot be applied to two different type of the field. So we used two

very different methods to for night vision images and X ray images. For night vision images

we try to apply natural color to images so that soldiers can relate the image to the surround

environment and looks familiar. So we prepared the database using natural reference image

and applied color transfer using that. In x ray we used the applied the standard color scales

defined in the literature and applied. We also developed a color look up table for coloring and

we observe that in this method we obtained the best result. In future we still find the another

method for colorization. The pre-processing on the grayscale images is very crucial and more

enhancing algorithms can be applied to obtain still better results.

References

[1]. Andreas Koschan and Mongi Abidi, "Digital Color Image Processing," A John Willy & Sons,

INC., Publication, Hoboken, New Jersey.

[2]. Rafael C. Gonzalez and Rechard E. Woods, “Digital Image Processing” Prentice Hall, New

Jersey.

[3]. Tanish Zaveri, Mukesh Zaveri, Ishit Makwana and Harshit Mehta ,“ An Optimized Region-based

Color Transfer Method for Night Vision Application”.

[4]. Toet. Natural color mapping for multiband nightvision imagenary. Information Fusion, vol. 4(3), pp. 155-166, 2003.

[5]. Besma R. Abidi, Senior Member, IEEE, Yue Zheng, Andrei V. Gribok, and Mongi A. Abidi, Member, IEEE. “Improving Weapon Detection in Single Energy X-Ray Images Through Pseudocoloring” Ieee transactions on systems, man, and cybernetics—part c: applications and Reviews, vol. 36, no. 6, pp. 784-796, November 2006.