A Novel Method for Image Enhancement using Median Based Filter

Transcription

1 Proc. of Int. Conf. on Control, Communication and Power Engineering 2010 A Novel Method for Image Enhancement using Median Based Filter Abstract This paper proposes a novel approach for image enhancement by median based brightness improving filter based on standard Histogram equalization (SHE). Initially it starts with equalization process where median brightness of the image is altered. Later, using median filter in the proposed approach enhances the quality of the image. The proposed method in despite of its simplicity has better results when compared to conventional SHE and other usual mean methods with respect to image quality, simplicity in implementation and less computation cost. Keywords- Image enhancement, Image equalization, Standard histogram equalization (SHE), Median, Mean. I. INTRODUCTION Image Enhancement is a process to bring out details in fine manner that are obscured, or to highlight certain features of interest in an image. This is done by changing the pixel s intensity of the input image, so that the output image should subjectively look better [1]. It plays an important role in improving the quality of an image if an image is having problems in its quality due to post decompression problems, blur, non equalization and other factors leading to the degradation of image quality. Histogram based techniques for image enhancement is mostly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Factors for the image degradation are poor Image equalization, noise, low illumination, optical distortions like camera shakes, blurring, and sensor distortion, sensor digitizer, data loss in the image which got transferred through wireless medium, atmospheric factors like haze, fog, turbulence, Compression ratios and artifacts. There are numerous number of image enhancement methods which have been proposed. Histogram equalization is commonly used for image enhancement in a number of applications because of its simplicity. A Standard Histogram Equalization (SHE) is one of the most commonly used algorithms to achieve image enhancement due to its simplicity [1]. The process of standard histogram equalization (SHE) involves remapping the gray levels of image based on probability distribution of the input gray levels. As a result the brightness of an image can be changed after the histogram equalization because of its flattening property leading to unnecessary visual distortions. But there are some problems with the standard Histogram Equalization (SHE). When histogram equalization is used, there is a serious drawback as the brightness of an image can be Brhmadesam Sateesh, Lead Software Engineer, Samsung India Software Centre, India sateesh.b@samsung.com changed after the histogram equalization because of its flattening property leading to unnecessary visual distortions. However, SHE causes the loss of definition on the edges of the subject and over enhancement of noise in the images [1] which gives a washed out look. Even though SHE is simple and popular, it is not suitable to be implemented in consumer electronics devices. In summary, the Problems with SHE are: (i) Flattening property of Histogram equalization is the biggest drawback leading to unnecessary visual distortions. (ii) It has mean-shift problem which means that the average brightness of the input image is considerably different from that of the output image (iii) It causes the loss of definition on the edges of the subject and over enhancement of noise in the images and (iv) The image data gets mixed with noise and image quality gets affected in a negative manner. 131 There are many image enhancement methods which have been already proposed. Hojat and Rezair [2] propose a contrast enhancement based histogram equalization (CEHE). The issue with this approach is we need to mention that we have subtracted background manually where the number of background pixels is more than other pixels. This is an extra computation step which can be avoided. Kim et al., [3] propose an enhancement method using partially overlapped sub-block histogram equalization (POSHE), best suited for less overlapped subblocks and it is computationally costly. Taek Kim [4] proposes a novel extension of histogram equalization, which they called as BBHE. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two sub-images obtained by decomposing the input image based on its mean with a constraint that the resulting equalized sub-images are bounded by each other around the input mean. With this there will be a uniformitivity issue. Mean method of preserving brightness of the pixel has an issue; the mean is sensitive to extreme values (called outliers). Mean, therefore, would not able to remove these outliers and affects by reducing the sharpness of the image. Kim and Chung [5] propose a novel approach called RSWHE of which its essential idea is to segment an input histogram into two or more sub-histograms recursively and modify them by means of a weighting process based on a normalized power law function and perform a Histogram Equalization

2 on weighted sub- histograms independently. This method is computationally costly and time consuming as it has to split the histograms into sub-histograms and weighting based as image quality is best measured through human perception rather a weighting function. After the recursive splits, there would be uniformitivity issue. Moreover, when mean or median are used they give practically different results. Ibrahim and Kong [6] propose a Brightness Preserving Dynamic Histogram Equalization (BPDHE) for Image Contrast Enhancement which is based on histogram partitioning, is a mean based approach for which the drawbacks have been mentioned in previously discussed papers. Ibrahim and Kong [7] propose a method of Image Sharpening Using Sub-Regions Histogram Equalization (SRHE) which focuses on sharpness of the image. But in the process image partition affects the final outcome with respect to picture quality on the basis of uniformitivity. Kim and Paik [8] propose a method of contrast enhancement where clipping rate is determined based on the mean brightness (GC-CHE), and the clipping threshold is determined based on the clipping rate. The clipping rate is adaptively controlled to enhance the contrast with preserving the mean brightness. It is mathematically proven that the mean brightness of the output image converges to that of the input image with adaptive controlled. But, in reality, the approach s brightness preserving way has a problem as it uses the mean method and the mean is sensitive to extreme values (called outliers). Mean, therefore, would not able to remove these outliers and affects by reducing the sharpness of the image. As sharpness reduces it affects the contrast directly. Wongsritong et al [9] proposed a method of contrast enhancement using multi-peak histogram equalization with brightness preserving (CE- MPHEBP), it is not specific about sharpness and Independent equalization for different peaks makes computation costly and it affects the visual quality as there are disparities in the image. Further, it has the same problem of mean like in [6], [8], [9] and [10]. Iyad Jafar, Hao Ying [10] has same issue related to mean like in [6], [8], [9]. Hossain and Alsharif [11] approach works well for certain amount of degree with respect to the number of images and gives a washed out look for some images. To overcome the above mentioned issues this paper proposes a novel method known as Brightness improving median based Enhancement algorithm [BIME]. Our proposed algorithm uses median approach for pixel brightness enhancement. In this work, more attention is given to simplicity in implementation and at the same time, providing high quality without losing any details from the image. The method needs to be simple to implement and also simultaneously solves the issues of brightness, contrast and sharpness. The rest of the paper is organized as follows. Section II explains the proposed Brightness improving median based enhancement (BIME) and section III describe the experimental results and finally the paper is concluded in Section IV. II. PROPOSED ENHANCEMENT USING MEDIAN BASED IMAGE EQUALIZATION FILTER The proposed Brightness improving median based equalization Filter (BIME) method involves following major steps: (i). Standard histogram equalization (ii). Gaussian filter (iii). Proposed Median based filter (iv). Normalization using median values of input and output images. A. Standard Histogram equalization In the first step the standard histogram equalization (SHE) is applied on the given gray scale image f (x, y). This method usually increases the global contrast of image as it is represented by close contrast values and allows for areas of lower local contrast to gain a higher contrast without affecting the global contrast. Thus SHE accomplishes this by effectively spreading out the most frequent intensity values. This method is indiscriminate as it increases the contrast of background noise, while decreasing the usable signal, which is its demerit. f (x, y) = P(r ) = B. Gaussian filter ; k = 0,.,., L 1. (1) Gaussian filter is applied to smoothen the obtained data from SHE so as to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures. f' (x, y) = exp ( A /2σ ); (2) Where, A is co-ordinate relative to the centre of kernel and σ, the standard deviation, is set to 1. 3, kernel size of 3 * 3 [6]. This is determined experimentally based on persistence of vision. Kernel size can be changed as per the input data. C. Median based filter To obtain a proper equalization and sharpening of the image, a new median based filter is proposed. This filter is based on log approach with the median values of the data obtained. The median needs to be taken from the data obtained from the output of Gaussian filter. This filter provides a better contrast introducing a good amount of sharpness and thus by increasing the quality of the image. f (x, y) = f (x, y) ( )/ ( ) (3) Where n is set to 3, which is the number of times the median is taken from the given image and M represents median of the image. D. Normalize image brightness using median approach Finally, the median brightness of the input image f (x, y) and the median brightness of the output image obtained from the previous step, f (x, y). 132

3 F (x, y) = (f(x, y) /f (x, y) ) * f (x, y) - (4) With median approach, the value of an output pixel is determined by the median of the neighborhood pixels, rather than the mean which has discussed in the said works. The median is much less sensitive than the mean to extreme values (called outliers). Median is therefore better able to remove these outliers without reducing the sharpness of the image. With this approach, we can get better visual representation by taking care of contrast and sharpness simultaneously leading to satisfying color enrichment. III. EXPERIMENTAL RESULTS AND DISCUSSIONS Apart from implementing the proposed approach, the mentioned eleven methods in introduction also have been implemented. Two different sized images have been chosen to demonstrate that the approach works for different sizes. The details of the images taken are as follow: Image 1. Mandapa Image size: 256 * 256 Image type: Uncompressed raw, 8-bit (unsigned char) grayscale image Image 2. Vimana Image size: 480 * 320 Image type: Uncompressed raw, 8-bit (unsigned char) grayscale image The results for two images have been shown in the paper for the mentioned approaches along with the proposed approach. Figures 1 &2 are shown with the said methods implemented. Here, we can see that the proposed filter solves the issues related to equalization contrast and sharpening. Here, in figure 1(m) & 2(m), we can see that the proposed approach provides pixel brightness enhancement and balance. And thus deals with the enhancement problem using median based filter approach to deal with the degradation of a digital image which has low quality with respect to brightness, contrast and sharpness. To show the effectiveness of proposed method in more clear way, quality objective measures have been applied. Finally the proposed method is compared with the mentioned methods in introduction, objective quality measures along with the mentioned eleven approaches: (i) Absolute median brightness difference (AMBD). AMBD is computed by AMBD = f (x, y) - f(x, y) * F(x, y)/n (5) Where f(x, y) is the given image and the other is f (x, y) which is obtained after step C and F(x, y) the processed final image, N is the total no of images used for testing the approaches. 133 (ii) Peak Signal to Noise Ratio (PSNR) The PSNR is defined as an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation. Here, it is used as an approximation to human perception of reconstruction quality. The PSNR is calculated as Where, MSE= PSNR = 10 * log ( ) (6) f(x, y) F(x, y) (7) Here f(x, y) is the given image and the other is F(x, y), the processed final image. The average values of the proposed and the compared methods are given in the Table 1. The following observations can be made from the contents of Table 1: 1. The average median intensity value is less for the proposed approach when compared to the other methods discussed as shown in the Table The PSNR value is high for the proposed approach when compared to the other methods discussed as shown in table 1. TABLE 1: AMBD & PSNR AVERAGE VALUES OBTAINED FROM 92 GRAY SCALE INPUT IMAGES. THE LOWER THE VALUE OF AMBD, THE BETTER IS THE QUALITY. THE HIGHER IS THE VALUE OF PSNR, THE BETTER IS THE QUALITY. Method AMBD PSNR Proposed approach BIME LTAHE[11] CVHE [10] CE-MPHEBP [9] GC-CHE [8] SRHE[7] BPDHE[6] RSWHE[5] BBHE[4] CEHE[2] POSHE[3] Standard Histogram Equalization(SHE)

4 Proc. of Int. Conf. on Control, Communication and Power Engineering 2010 (a)input image (d)cehe (g)rswhe (j)gc-che (b) SHE (c) LTAHE (e)poshe (f) BBHE (h)bpdhe (i)srhe (k) CE-MPHE-BP (l)cvhe Fig. 1. Results of all methods mentioned in introduction section I and the proposed method. Our area of interest includes mountains and the Pillared structure. As we can see, many of the methods suffer from flattening property, loss of definition on the edges of the subject in the image, here it is clearly shown in the proposed method (m), the said unwanted features are absent. (m) Proposed 134

5 (a) INPUT IMAGE (b) SHE (c) LTAHE (d) CEHE (e) POSHE (f) BBHE (g) RSWHE (h)bpdhe (i)srhe (j)gc-che (k) CE-MPHE-BP (m) Proposed (l)cvhe Fig. 2. Results of all methods mentioned in introduction section I and the proposed method. Our area of interest includes vimana structure and its background. As we can see, many of the methods suffer from flattening property, loss of definition on the edges of the subject in the image, here it is clearly shown in the proposed method (m), the said unwanted features are absent. 135

6 Hence, the proposed approach deals with problems which arise due to mean based approaches and Histogram Splitting approaches. The merits of the proposed approach can be summarized as less computational cost, does not reduce sharpness, easy implementation and maintains the minutest of the details and enhances the image quality. IV. CONCLUSIONS In this paper, a new method is proposed for image enhancement to increase the brightness level and at the same time improve the sharpness and have a proper balance. There are many approaches which have been mentioned here which gives good results but this proposed approach is a simple to be implemented in real time embedded system as it is less computational costly and a non-mean based approach providing good results. This approach simultaneously solves the enhancement and contrast issues. REFERENCES [1]. R.C. Gonzalez, R.E. Woods, Digital Image Processing, 3 rd edition, Prentice Hall [2]. Hojat Yeganeh, Ali Ziaei, Amirhossein Rezaie A Novel Approach for Contrast Enhancement Based on Histogram Equalization, in Proc. IEEE Int Conf on Computer and Communication Engineering, May 13-15, 2008 Kuala Lumpur, Malaysia,pp [3]. Joung-Youn Kim, Lee-Sup Kim, and Seung-Ho Hwang An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization,in IEEE transactions on circuits and systems for video technology, vol. 11, no. 4, april 2001,pp [4]. M. Yeong-Taekgi Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization, IEEE Transactions on Consumer Electronics, Vol. 43, No. 1, February [5]. Mary Kim and Min Gyo Chung Recursively Separated and Weighted Histogram Equalization for Brightness Preservation and Contrast Enhancement, [6]. Haidi Ibrahim and Nicholas Sia Pik Kong, Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement, in IEEE Transactions on Consumer Electronics, Vol. 53, No. 4, November [7]. Haidi Ibrahim and Nicholas Sia Pik Kong, Image Sharpening Using Sub-Regions Histogram Equalization, in Proc. IEEE Transactions on Consumer Electronics, Vol. 55, No. 2, May [8]. Taekyung Kim and Joonki Paik Adaptive Contrast Enhancement Using Gain-Controllable Clipped Histogram, in Proc. IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, November [9]. K. Dejhan, and A. Somboonkaew, "Contrast enhancement Using Multipeak histogram equalization with brightness preserving," IEEE Trans. Circuits and. Systems, pp , [10]. Iyad Jafar and Hao Ying Image Contrast Enhancement by Constrained Variational Histogram Equalization, IEEE EIT 2007 Proceedings. [11]. Md. Foisal Hossain, Mohammad Reza Alsharif Image Enhancement Based on Logarithmic Transform Coefficient and Adaptive Histogram Equalization in Proc. IEEE, 2007,International Conference on Convergence Information Technology. 136