o l . 4, issu e 2, apr i l - j 2013 issn : 2230-7109 (online) | issn ... · image enhancement,...

IJECT Vol. 4, IssuE 2, AprIl - JunE 2013 ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

w w w . i j e c t . o r g 118 InternatIonal Journal of electronIcs & communIcatIon technology

Power Law Transformation and Adaptive Gamma Correction: A Comparative Study

1Viney Dhawan, 2Gaurav Sethi, 3Virender Singh Lather, 4Khushneet Sohal1,2Lovely Professional University, Phagwara, Punjab, India

3,4KITM, Karnal, Haryana, India

AbstractThis paper describes the study of the image enhancement and provides the comparison between power law transformation and Adaptive Gamma Correction Weighted Distribution (AGCWD). In this paper, we will describe the importance of image enhancement and we will represent the two techniques or algorithm for image enhancement. In first algorithm power law transformation in which we will give the value of gamma manually, but in the second algorithm AGCWD (Adaptive gamma correction weighted distribution) in which the value of gamma will be optimize automatically. In this comparison of both techniques, we will show the qualitative and quantitative results.

KeywordsImage Enhancement, Histogram Equalization, RSWHE, Power Law Transformation, Adaptive Gamma Correction Weighted Distribution (AGCWD)

I. IntroductionImage enhancement plays an important role in the field of Digital image processing applications such as medical image analysis, remote sensing, scientific visualization and aerial images. In this paper we will describe the two techniques for image enhancement and will shows the comparison between them. Image enhancement is broadly classified into two broad categories spatial domain and frequency domain [1]. But our two algorithms are based on spatial domain. The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image. In power law transformation technique which is basically used to enhance the dimmed images [2], in PLT, we will use the gamma parameter to enhance the images. In this gamma parameter we will select the value of gamma manually in power law transformation but in the AGCWD in which the value of gamma will be optimized automatically based on the weighted distribution [3].We will describe the histogram equalization in the second section of this paper, because we utilize the histogram to enhance the images in the AGCWD approach in which we will describe the different histogram equalization techniques [4], which are basically used for image enhancement. In the third and forth section of this paper, we will describe the Power law transformation and AGCWD. In fifth section of this paper we will describe the results of both techniques in this section we will comparisons with the help of qualitative and quantitative results are used.

II. Histogram EqualizationHistogram equalization is an important image enhancement technique commonly used for contrast enhancement [4]. The histogram equalization technique is used to stretch the histogram of the given image. Greater is the histogram stretch greater is the contrast of the image. In other words if the contrast of the image is to be increased then it means the histogram distribution of the corresponding image needs to be widened. Histogram equalization is the most widely used enhancement technique in digital image

processing because of its simplicity and elegancy. In an image processing context, the histogram of an image normally refers to a histogram of the pixel intensity values.AGCWD used the histogram equalization techniques for image enhancement. As we know that Histogram Equalization (HE) is a very popular method for image enhancement [5]. Basically it stretches the dynamic range of input image by virtue of the cumulative distribution function, so we will get the image enhancement. But, HE has a problem of mean shift that is the mean brightness of the input image is significantly different from that of the output image. This mean shift problem is nuisance for consumer electronics products.To overcome such type of problems many other types of histogram equalization techniques are proposed. For the first time, Kim gives the solution of mean shift problem that has occurred in the HE technique and proposed BBHE (Brightness preserving Bi-Histogram Equalization) [6]. BBHE basically divides the first segments of the input histogram into two sub-histogram based on the mean of the input image’s brightness and then executes histogram equalization on each sub-histogram independently. Wan et al. proposed DSIHE (Dualistic Sub- Image Histogram Equalization), which is similar to BBHE except that the threshold for histogram segmentation is not the mean of the input image but the median of the input image brightness [7]. After that Chen and Ramli introduced the new histogram equalization technique is MMBEBHE (Minimum Mean Brightness Error Bi-Histogram Equalization) [8]. It determines the histogram segmentation threshold in such a way that the brightness difference between input and output image will be minimized.Chen and Ramli also proposed the method that is RMSHE (Recursive Mean Separate Histogram Equalization) which is the generalized of BBHE [9]. In BBHE we find the mean based histogram segmentation only once but in the case of RMSHE does it more than once recursively. Similarly Sim et al generalized DSIHE into RSIHE (Recursive Sub-Image Histogram Equalization) [10]. The media-based histogram segmentation takes place only once in DSIHE, but in the case of RSIHE occurs many times recursively. As we know that HE usually introduces two types of artifacts into the equalized image. First is over-enhancement for the image regions with more frequent gray levels. Second is the loss of contrast for the image regions with less frequent gray levels. So, the main motive of HE and all aforementioned algorithms have been more focused on the preservation of image brightness than the improvement of image contrast.As discussed above the HE techniques can be seen that these methods do not modify an input histogram at all. A new histogram equalization method, named RSWHE (Recursively Separated and Weighted histogram Equalization), to enhance the image contrast as well as preserve the image brightness. However, RSWHE changes the input histogram before running the equalization procedure. This is the fundamental difference between the previous methods and RSWHE. Specifically, RSWHE consists of three modules as given below figure:

IJECT Vol. 4, IssuE 2, AprIl - JunE 2013

w w w . i j e c t . o r g InternatIonal Journal of electronIcs & communIcatIon technology 119

ISSN : 2230-7109 (Online) | ISSN : 2230-9543 (Print)

Fig. 1: The Functional Block Diagram of RSWHE Method

As shown in block diagram of RSWHE, first module is histogram segmentation module that split an input histogram into two or more subhistograms recursively based on the mean and median of the image. Second module is histogram weighting module that changes the sub-histograms through a weighting process based on a normalized power law function. Last third module is histogram equalization module that equalizes the weighted sub-histograms independently.

III. Power-Law- TransformationPower-law transformations have the basic equation

(1)

(2)to account for an offset. However, offsets typically are an issue of display calibration and result they are normally ignored in (1). As in the case of the log transformation power-law curves with fraction values of gamma map a narrow range of dark input values into a wider range of output values. It has opposite being true for higher values of input levels. According to this, it will generate the curves with values of Ύ > 1 have exactly the opposite effect as those generated with values of Ύ < 1. Finally from (1) it reduces to the identity transformation when c = Ύ= 1. A variety of devices used for image capture, printing and display respond accordingto a power law. The exponent in the power law equation is referred to as gamma. The process is used to correct these power-law response phenomena is called gamma correction.Gamma correction is important if it is displaying an image an image accurately on a computer screen is of concern. Images that are not corrected properly can look either bleached out, or what is more likely too dark. It is trying to reproduce colors accurately also requires some knowledge of gamma correction because varyingthe value of gamma correction changes not only the brightness but also the ratios of red to green to blue. Basically in this method it utilizes for enhanced the dimmed images. In this method gamma is adjusted manually, according to this gamma value, the image will be enhanced. So the main drawback of this method is to give the value of gamma manually, it does not optimize the value of gamma. So, to remove this drawback with the help of AGCWD method that has been discussed in the next section.

IV. AGCWD (Adaptive Gamma Correction Weighted DistributionCorrection Weighted Distribution) In Power-law transformation method in which the main drawback is to give the value of gamma manually for image enhancement. This problem is to be solved by the Adaptive gamma correction weighted distribution method in which the value of gamma is find out automatically with the help of weighted distribution function. Gamma correction techniques make up a family of general HM techniques obtained simply by using a varying adaptive parameter Ύ. The simple form of the transform-based gamma correction is derived by

(3)Where lmax is maximum intensity of the input. The intensity l of each pixel in the input image is transformed as T (l) after utilize the Eq. (3). When the contrast is directly or manually modified by gamma correction then different images will results the same changes in intensity as a result of the fixed parameter. So this problem can be solved by probability density of each intensity level in a digital image can be calculated. The probability density function (pdf) can be approximated by

(4)Where n1 is the number of pixels that have intensity l and MN is total number of pixels in the image. The cumulative distribution function (cdf) is based on pdf, and is formulated as:

(5)After the cdf of the digital image is obtained from Eq. (5) traditional Histogram Equalization (THE) directly uses cdf as

(6)Fig. 2 shows the flowchart of proposed AGCWD method. Digital image used as input. After that the next step is to find the histogram of input image. In the third step weighted distribution function, the fluctuant phenomenon cab be smoothed, thus reducing the over-enhancement of the gamma correction. And last enhancedimage is at the output.The flow chart of proposed adaptive gamma correction as given:

Fig. 2: Flowchart of the AGCWD Method



The proposed Adaptive Gamma Correction (AGC) is formulated as follows:

(7)The Weighted Distribution (WD) function is formulated as:

Where α is adjusted parameter, pdfmax is the maximum pdf of the statistical histogram, and pdfmin is the minimum pdf. the modified cdf is approximated by

(8)Where the sum of pdfw is calculated as follows:

(9)Finally the gamma parameter based on cdf of Equation (7) is modified as follows:

(10)So, as we can see the upper equations of AGCWD that provides us Adaptive Gamma Correction and enhanced the dimmed and aerial images.

V. Experimental ResultsThis section presents the experimental results of both the techniques that are used for the enhancement of images. In our experiment we take the many different types of images as input like dimmed images, Aerial images. Through these images we can see the results of both the algorithms. In this section we will describe the two results that are Qualitative and Quantitative.

A. Qualitative ResultsIn this section of results, we will show the qualitative results. We take the input images of Aerial, Dimmed and another general purpose application images. It is important to qualitative assess the contrast enhancement. The major goal of the qualitative assessment is to judge if the output image is visually acceptable to human eyes and has a natural appearance.As shown in fig. 3. of airplane in which first image is original input image and results of both the techniques are shown in fig. 3. the PLT (Power Law Transformation) in which we take the value of gamma manually Ύ = 2.5 is given the value in the result of AGCWD in which it shows the result of optimize gamma thatAdaptive Gamma Correction Weighted Distribution (AGCWD). As shown in fig. 4, 5 and fig. 6. Results of road, aerial and pentagon images.

(a)

(b)

(c)Fig. 3: Enhancement Results for the Airplane Image. (a) Input Image (b) PLT Output Image when Ύ= 2.5 (c) AGCWD Output




(a)

(b)

(c)Fig. 4: Enhancement results for the Road image. (a) Input Image (b) PLT Output Image when Ύ= 0.5 (c) AGCWD Output.

(a)

(b)

(c)Fig. 5: Enhancement Results for the Aerial Image. (a) Input Image (b) PLT Output Image when Ύ= 0.5 (c) AGCWD Output



(a)

(b)

(c)Fig. 6: Enhancement Results for the Pentagon Image. (a) Input Image (b) PLT Output Image when Ύ= 0.5 (c) AGCWD Output

B. Quantitative ResultsIn this section we will describe the quantitative results of test images. These four test images are Airplane, Road, Aerial and pentagon. In order to detect small changes and get consistent results some quantitative methods are also used. These objective methods are statistical in nature and help us to measure the performance between two methods. Some of the objectives methods used are: In which find the values of Standard Deviation, Entropy and Average

Gradient between both the techniques.

Table 1: Comparison Table of Airplane Image

Table 2: Comparison Table of Road Image

Table 3: Comparison Table of Aerial Image

Table 4: Comparison of Pentagon Image

VI. ConclusionWe study the comparative analysis between Power-Law Transformation and AGCWD. These techniques are used for contrast enhancement. In PLT technique is also known as gamma correction method, but in which we give the value of gamma manually. But in the case of AGCWD in which we optimize the value of gamma with the help of weighted distribution. In weighting distribution is used to smooth the fluctuation phenomena and avoid the generation of unfavorable artifacts. Gamma correction can automatically enhance the image contrast through use of smoothing curve. According to above results we conclude that AGCWD method is better than the PLT.

References[1] Raman Maini, Himanshu Aggarwal,“A Comprehensive

Review of Image Enhancement Techniques”, Journal of Computing, Vol. 2, Issue 3, March 2010.




[2] T. Romen Singh,“Threshold Based Adaptive Power-Law Applications in Image Enhancement”, International Journal of Computer Applications, vol. 47, no. 7, June 2012.

[3] Shih-Chia Huang, Fan-Chieh Gneng and Yi-Sheng Chiu, “Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution”, IEEE Transactions on Image Processing, Vol. 22, No. 3, March 2013.

[4] Rajesh Garg,“Histogram Equalization Techniques for Image Enhancement”, IJECT Vol. 2, Issue 1, March 2011.

[5] Mary Kim and Min Gyo Chung,“Recursively Separated and Weighted Histogram Equalization for Brightness Preservation and Contrast Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 3, August 2008.

[6] Y. Kim,"Contrast Enhancement using Brightness Preserving Bi- Histogram Equalization”, IEEE Transaction on Consumer Electronics, Vol. 43, No. 1, 1997, pp. 1-8.

[7] Y. Wan, Q. Chen, B. Zhang,"Image Enhancement on Equal Area Dualistic Subimage Histogram Equalization Method”, IEEE Transactions on Consumer Electronics, Vol. 45, No. 1, 1999, pp. 68-75.

[8] S. Chen, A. R. Ramli,“Minimum Mean Brightness Error Bi-histogram Equalization in Contrast Enhancement”, IEEE Transaction on Consumer Electronics, Vol. 49, No. 4, 2009, pp. 1310-1319.

[9] S. Chen, A. R. Ramli,“Contrast Enhancement using Recursive Mean- Separate Histogram Equalization for Scalable Brightness Preservation”, IEEE Transaction on Consumer Electronics, Vol. 49, No. 4, 2003, pp. 1301-1309.

[10] K.S.Sim,"Recursive Sub-image Histogram Equalization Applied to Gray-Scale Images”, Pattern Recognition Letters, Vol. 28, 2007, pp. 1209-1221.

Viney Dhawan received the B.Tech.Degree (with first class honors) in electronics & Communication engineering, in 2008 from Doon Valley institute of Engineering and Technology affiliated from Kurukshetra University. And presently pursuing 3 years part time MTech in Electronics and Communication from LPU, University, Phagwara, Punjab. He is also doing a teaching in Electronics

and Communication Department, Karnal Institute of Technology & Management, Karnal. His interest area is Image processing. Currently, he is working on improvement of one of the enhancement technique that is Gamma correction technique.

o l . 4, issu e 2, apr i l - j 2013 issn : 2230-7109 (online) | issn ... · image enhancement,...

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