Contourlet Transform and Histogram Equalization for Brightness Enhancement of Color Image

8/12/2019 Contourlet Transform and Histogram Equalization for Brightness Enhancement of Color Image

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CS

CInternational Journal of Computer Networks and Communications Security

VOL. 1, NO. 4, SEPTEMBER 2013, 140143

Available online at: www.ijcncs.orgISSN 2308-9830

Contourlet Transform and Histogram Equalization for Brightness

Enhancement of Color Image

Khalil Ibraheem AlSaif1and Ahmed S. Abdullah

2

12Dept. of Computer Science College of Computer & Mathematic Science-Mosul Univ./ IRAQ

E-mail: [email protected], [email protected]

ABSTRACT

For decades, several image enhancement techniques have been proposed. Although most techniques require

profuse amount of advance and critical steps, the result for the perceive image are not as satisfied, In this

paper a new approach for enhancing brightness of color image based on contourlet transform and histogram

equalization proposes. The color image is converted to HSI (hue, saturation, intensity) values. The i, which

represent the luminance value of color image, decomposed to its coefficients by non-sampling contourlet

transform, then applying grey-level contrast enhancement technique on some of the coefficients. Then,

inverse contourlet transform is performed to reconstruct the enhanced S compoment. The S component is

enhanced by histogram equalization while the H component does not change to avoid degradation color

balance between the HSI components. Finally the enhanced S and I together with H are converted back to

its original color system. The new approach gives Brightness enhancement more than 20% when was

applied on different type of images and tested the performance.

Keywords: Image Processing, Image Enhancement, Brightness Enhancement, Contourlet Transform ,

HSI Color Space.

1 INTRODUCTIONImage enhancement is a technology to improve

the quality of an image in terms of visual

perception of human beings [1]. With the growing

quality in image acquisition, image enhancement

technologies are more and more needed for many

applications [2]. Images are categorized into grey-

level images and color images. Each pixel of the

grey-level image has only one grey-level value as

opposed to color images pixels; therefore, there

have been many algorithms for contrast enhance-

ment for grey-level images. The main techniques

for image enhancement such as contrast stretching,

slicing, histogram equalization, for grey-level

images are discussed in many articles and books.

On the other hand, since each pixel of color images

consists of color information as well as grey-level

information, these typical techniques for grey-level

images cannot be applied to color images. Thus,

compared with grey-level images, the enhancement

of color images is more difficult, and there are

much more points to be researched.

Some color enhancement methods were proposed

based on histogram equalization [3]. An enhance-

ment algorithm is one that yields a better-quality

image for the purpose of some particular

application which can be done by either suppress-

ing the noise or increasing the image contrast and

brightness. Image enhancement algorithms are

employed to emphasized, sharpen or smoothen

image features for display and analysis. Enhance-

ment methods are application specific and are often

developed empirically. The enhancement process

does not increase the inherent information content

in the data but it does increase the dynamic range of

chosen features so that they can be detected easily

[4].

2 RELATED WORKIn 2013, khalil alsaif and ahmed saadi presented

in their research "color image enhancement based

on contourlet transform coefficients", amethod to

enhance the color image based on contourlet

transform after convert the color space from (RGB)


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to (HSV) and applied contourlet on valuecomponents.

In 2011 , Muna F. Al-Samaraie and NedhalAbdul Majied Al Saiyd presented in their research," Colored Satellites Image Enhancement UsingWavelet and Threshold Decomposition", a methodto improve the color of satellites image based onwavelet transform after convert the color imagefrom (RGB) color space to Gray scale.

Kartik Sau, Amitabha Chanda and Milan Pal, In2010, presented in their research "color imageenhancement based on wavelet transform andhuman visual system". A schema to enhancing thecolor image, contrast enhancement technique appl-ied on approximate component of wavelet transfo-rm after applied on intensity component (Kartik etal, 2010).

In 2008, Li He and You Yang presented in their

research" An Improved Color Image EnhancementAlgorithm Based on MSR". The appropriatewavelet bases were selected to decompose the inputimage into three levels. Then different enhancementalgorithms were employed to process the decomp-osed wavelet coefficients and scale coefficients, forthe scale coefficients, the MSR algorithm was used(Li and you, 2008).

In 2007 , Ding Xiao and Jun Ohya presented intheir research " Contrast Enhancement Of ColorImages Based On Wavelet Transform And HumanVisual System", a method to improve the contrastof color image, wavelet transform applied on value

component ,the approximate components enhancingby using contrast enhancement technique based onhuman visual System(Ding and Jun,2007).

in 2007, "Color image enhancement based onsingle-scale retinex with a JND-based nonlinearfilter" , an input RGB color image is transformedinto HSV color image and the S and V componentimages are enhanced [5].

Choi et al. in 2008 proposed Color Imageenhancement using single scale retinex based on animproved information model, in which all the

processing was done in the HSV color space

3 COLOR SPACEColor provides a significant portion of visual

information to human beings and enhances theirability of object detection. In black and whiteintensity image, the visual stimulus covers theentire bandwidth of the visible spectrum rangingfrom 0.4 micrometer to 0.7 micrometer. If we

narrow down the bandwidth and vary the centralwavelength, different colors are seen. It isexperimentally estimated that the human eye candistinguish above 3, 50,000 different colors. Asystematic way of representing and describingcolors is a color model. If the visible portion of thelight spectrum is divided into three components thedominant colors are red, green and blue, then thoseare considered as the primary colors of the visiblelight spectrum. In RGB-color model the colors arespecified by the amounts of the red, green and bluecomponents present in the color. This model iscalled additive and subtraction model because anycolor in this model, can be defined using theweighted (weights are non-negative) sum of R, Gand B components. In HSI model the informationabout the color is described in terms that aremorefamiliar to humans. In HSI color space, the

color is decomposed into hue, saturation andIntensity values, which is quite similar with the way

by which human tends to perceive color. Amongthe components of HSI color space, hue is theattribute of a color, which describes which color itis [3]. During the process of enhancement, it must

be seen that hue should not be changed for anypixel. If hue is changed then the color gets changed;thus the image gets distorted. Compared with theother perceptually uniform color space such as CIE,LUV and CIE lab, it is easier to control the huecomponent of color and avoid color shifting in theHSI color space [1]. In the algorithm, the hue is

kept preserved and enhancement technique isapplied to saturation(S) and intensity (I) compone-nts only, to enhance the contrast as well as brightn-ess of the image. We apply our enhance-mentmethod in HSI color space. In general, color imagesare represented by RGB color space. Therefore thefirst step is to convert RGB color space to HSIcolor space. The conversion algorithm is shown infig(1) which show a complete relation between thetwo color model.


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Do and Vetterli[7;8] refer to function f (lj

)j;k;n(t)gn2Z2 as contourlets. The indexes j; k; and

n are for the scale, direction, and location,

respectively. Fig. 2 illustrates the 3-level contourlet

decomposition for Zoneplate image.

5 BRIGHTNESS ENHANCEMENTPHASES

Step 1: read the color image (RGB colorspace).

Step 2: convert (RGB) color space to (HSI)color space and focus on components like H, S

and I .

Step 3: apply nonsubsampling contourlettransform on I complement.

Step 4: apply enhancement techniques(contrast stretch) to coefficients.

Step 5: Reconstruct I by inverse nonsubsamp-ling transform.

Step 6: Apply the Histogram Equalization onS complement.

Step 7: Now H component, modified S andmodified I components converted to RGB

color space.

6 APPLIED EXAMPLETesting the performance of proposed algorithm,

by applying it on a low brightness color images

and a dark color images and compare the results

with original image, Fig 3 show the experimental

result on different images [1]

5 REFERENCES[1] Khalil Ibraheem Al-Saif and Ahmed S.

Abdullah, Color Image Enhancement Basedon Contourlet Transform Coefficients,

Australian Journal of Basic and Applied

Sciences, 7(8): 207-213, 2013.

[2] M.Balaji, V.Kamaraj. Differential EvolutionOptimization Combined With Chaotic

Sequences for Optimal Design of Switched

Reluctance Machine. Journal of Theoretical

and Applied Information Technology

[3] Xiao, Ding, and Jun Ohya. "Contrastenhancement of color images based on wavelet

transform and human visual system." In

Proceedings of the IASTED International

Conference on Graphics and Visualization in

Engineering, pp. 58-63. ACTA Press, 2007.

[4] Sharmila, R., and R. Uma. "A New ApproachTo Image Contrast Enhancement using

Weighted Threshold Histogram Equalization

with Improved Switching Median Filter."

International Journal Of Advanced Engineering

Sciences and Technologies Vol 7: 206-211.

[5] Shen, Chih-Tsung, and Wen-Liang Hwang."Color image enhancement using retinex with

robust envelope." In Image Processing (ICIP),

2009 16th IEEE International Conference on,

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[6] Zheng YA, Zhu CS, Song JS, et al. Fusion ofmulti-band SAR images based on contourlet

transform. [C]//IEEE International

Conference on Infor- mation Acquisition.

NY:IEEE, 2006: 420424.[7] Do, Minh N., and Martin Vetterli.

"Contourlets: a new directional multiresolution

image representation." In Signals, Systems and

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[8] Wei, Xiao-lei, Yong-an Zheng, Zhan-zhongCui, and Quan-li Wang. "SAR Images

Denoising Based on Rough Set Theory in

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521-525. IEEE, 2007.[9] Zheng YA, Zhu CS, Song JS, et al. Fusion of

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Contourlet Transform and Histogram Equalization for Brightness Enhancement of Color Image