24 Bit Image Noise Reduction with Median Filtering Algorithm
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1 24 Bit Image Noise Reduction with Median iltering Algorithm Harmayani 1, Robbi Rahim 2* 1 Departement o Computer Engineering, AMIK Intel Com Global Indo Kisaran, Jl. Graha Abdi Satya No 4-48, Kisaran, Indonesia 2 Departement o Computer Engineering, Medan Institute o Technology, Jl. Gedung Arca No.52, Medan, Indonesia, Abstract: noise in the image is a common thing to happen because o many actors, to the reduction o noise can use special sotware that already applies some algorithms, one algorithm that can be utilized is the median iltering, algorithms median can be used to perorm a wide variety o noise reduction on the image, one o which is the salt and pepper noise commonly ound in color image and by using median iltering noise will reduce. Keyword: Noise, Noise Reduction, Median iltering, Salt and Pepper Noise, Noise Reduction Algorithm I. INTRODUCTION Image processing process on many kinds but in general, image processing can classiy into several types, image enhancement is one o the process [1] [2] [3] [4]. Image enhancement aims to obtain images with better quality than the original image [5]. This process seeks to improve the quality o the image by manipulating pixels or objects in the picture [4] [6]. One type o image enhancement is noise reduction [] [8]. Image may have noise caused by environmental actors, or channel data delivery were not good [8], the noise in the picture usually in the orm scattered points all over the image or only partially [1] [4], reduction o noise is a technique that can be used to correct the noise in an image by changing the pixel noise o image with a pixel image that is not aected by noise [4] [], median iltering algorithm could be used in the noise reduction process by taking the median value o a neighboring pixel values that exist in an image, in this research using Salt and Pepper Noise []. II. THEORY 2.1 Digital Image Processing Digital image processing is a process o changing the shape o the original image into image in another orm in accordance with desired [2] [3] [4] [], with digital image processing, an image that degraded, the color is too much contrast, less sharp, blur, and else, can be manipulated into image quality better [6] [] than beore or also change a picture that has either become blurred, etc. 2.2 Median iltering Median iltering is included into ilter types Mask Processing [1] [4] [] [8], where the workings o the algorithm is to calculate the value o pixels within the window o neighborhood in the image [], an image is a pixel which has the intensity value quantization result o the digital equipment. Image has three color components which each color component storage use 8 bits or 1 byte, it means the number o bits in one pixel is as much as 3 x 8 bits = 24 bits [2], so to calculate the number o pixels o an image by dividing the image intensity values with 24 bit, noise reduction with median iltering algorithm using the ollowing ormula (x, y) = ( r (x, y) + g (x, y) + b (x, y) ) All Rights Reserved 1
2 Volume 3, Issue 2; ebruary - 21 [ISSN: ] Based on the ormula above that the examination o noise in the image carried in stages starting rom the beginning o a matrix pixel to pixel until the end o a matrix. III. RESULT AND DISCUSSION Noise in the picture will be eliminated by median iltering using detection rules noise, by evaluating each point in the picture by orming a spatial window 3x3 uses rules to detect noise by comparing the average o the same pixel by pixel center, the ollowing is an example o the value o a point in an image contained in the picture has a noise, a value below is just an example rom 24 Bit image color to prove the value o the application o median iltering method. Table 1. Value Point On Image The above table is that the authors assume the pixel value as the value o the pixel image, to take the pixel values o a picture can not be done by hand still need special sotware like Matlab. rom Table 1 above created table 3x3 matrix to calculate the value o noise based on the center point o noise, the process is as ollows: Table 2. Matrix 3x3 Position (1,1) The 3x3 matrix o the table carried the ollowing ormula to check noise ( ) = ( x).exp [ ] ( ) = ( x) ( ) = ( ) + ( 1) + ( 2) + ( 3) + ( 4) + ( 5) + ( 6) + ( ) / x ( ) = ( )/ 9 ( ) =81.88= 82 urthermore, the center point o the window shits to the position (1,2). The same steps were perormed to calculate the average value o the center point o the window, the value o () = 82 is the value that will replace the median value o the value matrix image, in this case, is the ith that gave a color marker yellow, numbers 56 is replaced with 82 which is the result o the calculation o median All Rights Reserved 2
3 Volume 3, Issue 2; ebruary - 21 [ISSN: ] Table 3. Matrix 3x3 Position (1,2) Here is the median iltering process:. ( ) = ( x).exp [ ] ( ) = ( x) ( ) = ( ) + ( 1) + ( 2) + ( 3) + ( 4) + ( 5) + ( 6) + ( ) / x ( ) = ( )/ 9 ( ) =14.88= 15 urthermore, the center point o the window shits to the position (1,3). The same steps were perormed to calculate the average value o the center point o the window. Table 4. Matrix 3x3 Position (1,3) Shown in Table 3.3 pixel values have been changed to 5, which previously was 6, the ollowing is the process or the position (1,3) x.exp x x / ( ) = ( )/ 9 ( ) =86.5= 86 urthermore, the center point o the window shits to the position (1,4). The same steps were perormed to calculate the average value o the center point o the All Rights Reserved 3
4 Volume 3, Issue 2; ebruary - 21 [ISSN: ] Table 4. Matrix 3x3 Position (1,4) Shown in Table 3.4 pixel values have been changed to 6 previously was 16, the ollowing is the process or the position (1,4) x.exp x x / ( ) = ( )/ 9 ( ) =.8= 1 urthermore, the center point o the window shits to the position (1,5). The same steps were perormed to calculate the average value o the center point o the window. Table 5 Matrix 3x3 Position (1,5) Here is the process or the position (1,5) x.exp x x / ( ) = ( )/ 9 ( ) =39.88= 4 Process improvements have been made to move the image to produce a matrix table as ollows: Table 6 Results o End Process All Rights Reserved 4
5 Volume 3, Issue 2; ebruary - 21 [ISSN: ] The above process is still in a 3x3 matrix on line 1.3, and or the same steps until the end o the process carried last bit until all bits are checked, the selection o a 3x3 matrix due to the process o the count 3x3 matrix processed value is not much and the median value is more easily obtained than the value matrix bigger such as 5x5 or 1x1. IV. CONCLUSION Reduction o noise by using median iltering using spatial window 3x3 can run well and the results o the calculation process proposed is dierent rom the median iltering can usually reduce the noise quite well in color images o 24 bits, the development o algorithms median iltering is quite a lot and can be applied to various noise case. REERENCES [1] K. O. Boateng, B. W. Asubam, and D. S. Laar, "Improving the Eectiveness o the Median ilter," International Journal o Electronics and Communication Engineering, vol. 5, no. 1, pp. 85-9, 212. [2] D. Noriansyah and R. Rahim, "COMBINATION O PIXEL VALUE DIERENCING ALGORITHM WITH CAESAR ALGORITHM OR STEGANOGRAPHY," International Journal o Research In Science & Engineering, vol. 2, no. 6, pp , 216. [3] D. Apdilah, M. Y. Simargolang and R. Rahim, "A Study o rei-chen Approach or Edge Detection," International Journal o Scientiic Research in Science, Engineering, and Technology (IJSRSET) - See more at vol. 3, no. 1, pp , 21. [4] E. S. Ahmed, R. E. A. Elati, and Z. T.Alser, "Median ilter Perormance Based on Dierent Window Sizes or Salt and Pepper Noise Removal in Gray and RGB Images," International Journal o Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 1, pp , 215. [5] N.Sakthivel and L.Prabhu, "Mean Median iltering or Impulsive Noise Removal," International Journal o Basic and Applied Science, vol. 2, no. 4, pp. 4-5, 214. [6] R. Mehta and N. K. Aggarwal, "Comparative Analysis o Median ilter and Adaptive ilter or Impulse Noise A Review," International Journal o Computer Applications, vol. 4, no. 11, pp , 214. [] A. Ikhwan and R. Rahim, "Implementation o Modiied Median iltering Algorithm or Salt & Pepper Noise Reduction on Image," The International Journal O Science & Technology, vol. 4, no. 11, pp. 5-9, 216. [8] P. K. Garg, P. Verma, and A. Bhardwaz, "A Survey Paper on Various Median iltering Techniques or Noise Removal rom Digital Images," American International Journal o Research in ormal, Applied & Natural Sciences, pp. 43-4, All Rights Reserved 5
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