INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

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1 INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue 2, July- September (2012), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJECET I A E M E SPATIAL DOMAIN IMAGE ENHANCEMENT USING PARAMETERIZED HYBRID MODEL 1 I.Suneetha and 2 Dr.T.Venkateswarlu 1 Associate Professor,ECE Department,AITS,Tirupati,INDIA Pin Professor,ECE Department,S.V.University College of Engineering,Tirupati,INDIA Pin iralasuneetha.aits@gmail.com, 2 warlu57@gmail.com Abstract Images are very powerful tools to provide information to the viewers in every field i.e. medical images for doctors, forensic images for police investigation, text images for readers etc. In the process of image acquisition, contrast of an image becomes poor because of lighting, weather, distance, or equipment used for image capture. Noise corrupts the images during sensing with malfunctioning cameras, storing in faulty memory locations or sending through a noisy channel. Sometimes quality of the image may be corrupted by poor contrast and unwanted noise. This paper proposes a method for image enhancement through contrast improvement and noise suppression using a Parameterized Hybrid Model in spatial domain. The proposed method provides good results subjectively as well as objectively for both gray scale and true color images. The proposed method is better, faster, and also useful for interactive image processing applications as it provides various enhancement images for an image. Key Words-Parameterized Gradient Intercept (PGI), Parameterized Adaptive Recursive (PAR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Digital Image Processing(DIP). I. Introduction Image enhancement improves digital image quality without knowing the source of degradation and provides visually acceptable images for human viewers and/or automated image processing techniques. We reviewed enhancement techniques for gray scale images in spatial domain and implemented 209 them using MATLAB [1]. These techniques have been extended successfully to true color images [2]. Image enhancement through noise suppression can be done using a Nonlinear Parameterized Adaptive Recursive (PAR) model [3]. Image enhancement through contrast improvement can be done by using a Linear Parameterized Gradient Intercept (PGI) model [4]. Linear

2 and nonlinear models work well when an image is corrupted by either poor contrast or unwanted noise, but fails when corrupted by both. This paper proposes a method for image enhancement through contrast improvement and noise suppression using a Parameterized Hybrid model in spatial domain. The type of noise considered is salt and pepper noise. Sections II and III cover related work done about linear PGI model and nonlinear PAR model. II. Linear PGI Model Relation between the input image and output image in a linear PGI model is g x,y =G f x,y +I 0 x< 0 y< where G is Gradient and I is Interception of the transformation. G and I values can be zero, positive, or negative. When G and/or I values are varied for improving the image contrast, above transformation becomes simple linear or nonlinear but not exponential or logarithmic as in traditional point processing methods and does not require PDF calculations as in histogram processing operations. PGI model works well for a gray scale image and results are much more pronounced for true color image by preserving maximum color details. Results indicate that mean square error (MSE) and Computational time (tc) of PGI method is smaller when compared to Traditional Histogram Equalization (THE) and Adaptive Histogram Equalization (AHE) methods. (a) (b) Fig. 1: (a) Man image, darken image, THE image, AHE image and PGI image (b) Their Histograms Table 1: MSE and t c for Man image MSE t c THE AHE PGI THE AHE PGI e III. Nonlinear PAR Model The relation between input image and output image for nonlinear PAR model is g(x,y) = imed[f n (x,y)] where imed means Intentional median filter that performs filtering to noisy pixels intentionally. Let A be the window size that is adaptive and R be the Recursive order. A 210

3 and/or R can be varied. Results indicate that PAR method has small t c and high PSNR when compared to TMF, RMF, and AMF. Table 2: PSNR and t c for Man image PSNR(dB) Original TMF RMF AMF PAR tc( sec) TMF RMF AMF PAR combinable to enhance an image to that is corrupted by both poor contrast and unwanted noise. As we are combining a linear and nonlinear model, the resultant model can be named as Parameterized Hybrid Model (PHM) in which G and/or I, A and/or R are varied. The proposed PHM has smallest MSE and highest PSNR at low computational cost in spatial domain. The following are the steps involved in PHM algorithm simulation. Gray scale image: (a) (b) (c) (d) (e) (f) Fig. 2: (a) Man image (b) noisy image (c) TMF (d) RMF (e) AMF, and (f) PAR images IV. Proposed Method PGI model improves contrast with smallest mean square error and low computational time. PAR model suppresses noise with highest PSNR and low computational time. Hence linear PGI model and nonlinear PAR model can be 1. Consider a good contrast and noiseless image i(x,y). 2. Get poor contrast image f c (x,y) by amplitude scaling of i(x,y) 3. f n (x, y) is a noisy image of f c (x,y). 4. Select appropriate values of A and R. 5. Ensquare noisy image with (A-1)/2 zeros to get padded image f p (x,y) 6. g p (x,y) is imed filter of f p (x,y). 7. If g p (x,y) is noisy, vary A and/or R. 8. If A varies, go to 4 th step otherwise go to 5 th step. 9. Remove the ensquared zeros in g p (x,y) to get denoisy image f dn (x,y). 10. Select appropriate values of G and I. 11. Multiply f dn (x,y) by G and add I to get g(x,y) 12. Observe the enhanced image g(x,y). 13. If g(x,y) is not good in contrast, then change G and/or I, go to11 th step. True Color image: 1. Consider a good contrast and noiseless image i(x,y). 2. Get poor contrast image f c (x,y) by amplitude scaling of i(x,y) 3. f n (x, y) is a noisy image of f c (x,y). 4. Select appropriate values of A and R. 5. Extract r,g,b components from f n (x,y) 211

4 6. Select appropriate values of A and R. 7. Ensquare noisy rgb images of f n (x,y) with (A-1)/2 zeros to get padded images r p g p b p. 8. Perform imed filter to r p g p b p separately. 9. Get color image g p (x,y) from filtered r p g p b p. 10. If g p (x,y) is noisy, vary A and/or R. 11. If A varies go to 7 th step otherwise go to 8 th step. 12. Remove the ensqured zeros in g p (x,y) to get denoisy image f dn (x,y). 13. Extract Y from f dn (x,y) using RGB to YIQ conversion to get l(x,y). 14. Select appropriate values of G and I. 15. Multiply l(x,y) by G and add I to get f(x,y). 16. Get enhanced image g(x,y) from f(x,y) using YIQ to RGB conversion. 17. Observe the enhanced image g(x,y). 18. If g(x,y) is not good in contrast, then change G and/or I, go to15 th step. V. Results The PHM performance can be verified by not only by visual inspection of the resultant images but also by evaluating the mean square error and Peak Signal to Noise Ratio in decibels (PSNR) [5-7]. The subjective results and objective results are shown in the following figures and tables. =20 1 = 1,, 212

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6 International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 (a) (b) Fig. 3: (a) Darken and noisy gray scale images (b) Parameterized Hybrid Model images (a) (b) Fig. 4: (a) Darken and noisy true color images (b) Parameterized Hybrid Model images 214

7 International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 (a) (b) Fig. 5: (a) Brighten and noisy gray scale images (b) Parameterized Hybrid Model images (a) (b) Fig. 6: (a) Brighten and noisy true color images (b) Parameterized Hybrid Model images. 215

8 Visual inspection of subjective results indicates that, the Parametric Hybrid Model works very well by enhancing gray scale and true color images that are corrupted by decreased contrast and unwanted noise. Visual inspection of objective results shows that, MSE was decreased and PSNR was increased for the gray scale images and also for the R, G, and B components of true color images. The limitation in the proposed model is small decrement in mse and small improvement in PSNR for enhancing gray scale and true color images that are corrupted by increased contrast and unwanted noise. The reason is, while increasing contrast for getting simulation results some pixel values reach maximum value which are then treated as salt during denoising. Therefore resultant images are not having very good contrast. This problem can be overcome by slight change in PHM algorithm. VI. Conclusions Spatial Domain Image enhancement using Parameterized Hybrid Model has been successfully implemented using MATLAB. This paper considers gray scale and true color images from different fields. Choice of A and R depends on noise intensity where as choice of G and I depend on amount of poor contrast. As proposed algorithm is a faster and better, PHM can be used as a tool for Photo editing software like Photoshop or any existing image processing software by attaching two sliding bars for A and R that suppresses noise and two sliding bars for G and I that improves contrast. The PHM model can be used for suppressing high level salt and pepper noise or other types of noises with slight changes in algorithm. Future scope will be the development of local parameterized models for image enhancement in Region Of Interest (ROI) when an image is corrupted differently in various regions. REFERENCES [1] Ms. I.Suneetha and Dr.T.Venkateswarlu, Enhancement Techniques for Gray scale Images in Spatial Domain, International Journal of Emerging Technology and Advanced Engineering, website: ) Volume 2, Issue 4, April 2012, pp [2] Ms. I.Suneetha and Dr.T.Venkateswarlu, Enhancement Techniques for True Color Images in Spatial Domain, International Journal of Computer Science & Technology (IJCST), Website: ) Volume 3, Issue 2, Version 5, April - June 2012, pp [3] Ms. I.Suneetha and Dr.T.Venkateswarlu, Image Enhancement Through Noise Suppression Using Nonlinear Parameterized Adaptive Recursive Model, International Journal of Engineering Research and Applications (IJERA), Website: (ISSN ), Volume 2, Issue 4, July-August 2012, pp [4] Ms. I.Suneetha and Dr.T.Venkateswarlu, Image Enhancement Through Contrast Improvement Using Parameterized Gradient Intercept Model, ARPN Journal of Engineering and Applied Sciences (ARPN- JEAS), Website: (ISSN ), Volume 7, No. 8, August [5] J Rafael C Gonzalez, Richard E. Woods, and Steven L. Eddins, Digital Image Processing Using MATLAB (Second Edition, Gates mark Publishing, 2009). [6] J. Y. im, L. S. Kim, S. H Hwang, An advanced Contrast Enhancement Using Partially Overlapped Sub Block Histogram Equalization, IEEE Transactions on Circuits and Systems for Videc Technology, Vol. 11, No. 4, pp ,2001. [7] Prof. A. Senthilrajan, Dr. E. Ramaraj, High Density Impulse Noise Removal in Color Images Using Region of Interest Median Controlled Adaptive Recursive Weighted Median Filter, Proceedings of the International MultiConferenceof Engineers and Computer Scientists (IMECS), Vol. II, March 17-19, 2010, Hong Kong. 216