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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 841 Image Restoration using Adaptive Median Filtering Hetvi Soni[1], Darshana Sankhe[2] Student[1], Professor[2], Dept. Of Electronics Engineering, D.J Sanghvi College of Engineering, Mumbai, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – In the field of image processing, image restoration is the most essential task. Image often gets corrupted due to which there is presence of noise in the image. Generally median filter is used to remove the presence of such noise but median filter works fine for about intensity of 20% noise in the image. So in order to get a better image restoration we can use another image restoration technique which is adaptive median filtering which works very well for noise intensity beyond 20% .The benefit of adaptive filter over median filter is that it does not erode away edges or small details in the image. Key Words: Adaptive median filter, Image Processing, Median filter, Salt and Pepper Noise, Gaussian Noise PSNR. 1. INTRODUCTION In the field of image processing image gets corrupted during image transmission or acquisition stage due to various influencing parameters such as faulty device etc. Such type of corruption gives rise to noise in the image which affects the information present in the image. The various type of noise that arises in the image is impulse noise also know as salt and pepper noise, Gaussian noise, and speckle noise to name a few [1]. In order to retrieve the original information from the image and eliminate noise from the image we need to apply certain de-noising techniques. These de- noising techniques make use of kernel which is made to convolve over the image and the result obtain through this convolution is a noise free image. The size of kernel (window) used for the same varies and intended output with same also varies. One such commonly used technique is median filtering. Median filtering works fine when the noise intensity is less but it starts to fail when the noise intensity in the image is high [3]. In order to overcome this problem we can make of spatial filtering technique. One such Filtering algorithm used is adaptive median filter. Adaptive median filtering is better than median as it is a two step filtering technique. The main advantage of adaptive median filter is that the behavior of the adaptive filter changes depending on the characteristics of the image under filter. Other main feature of adaptive filter is that it works well not only for impulse noise but also for speckle noise and Gaussian noise [3]. 2. MEDIAN FILTER Median filter is the most commonly used filter. It is a non linear method of filtering. The size of the kernel can be of nxn size which is made to convolve or slide over a mxm corrupted image. While performing this operation the median value of nxn kernel on the image is obtained and then the value of a particular pixel is replaced with the median value of the nxn kernel. Fig -1: Sorting in median filter. 2.1. Drawbacks of median filter: Effective only when the noise is impulse noise (salt and pepper). Its output quality deteriorates when the noise is more than 20%. It does not work efficiently when the spatial density of noise is high. For large kernel size, there is no proper smoothening of the image instead valuable information from the image gets blur.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 842 2. ADAPTIVE MEDIAN FILTER The adaptive median filter is designed to eliminate the drawbacks faced by the standard median. The main advantage of adaptive median filter is the size of the kernel surrounding the corrupted image is variable due to which better output result is obtained. The other main advantage of adaptive filter is that unlike median filter it does not replace all the pixel values with the median value. The working of adaptive filter is a two step process; in the first step its finds the median value for the kernel and in the second step it checks whether the current pixel value is a impulse (salt and pepper noise) or not. If pixel value is corrupted then it changes it value with median or else it retains the value of the gray scale pixel. By doing this, one can ensure that only the pixels with impulse noise is changed while all other pixel values are retained as they are. 3.1. Implementation of adaptive median filter Zmin = Minimum gray level value in Sxy. Zmax = Maximum gray level value in Sxy Zmed = Median of gray levels in Sxy Zxy = gray level at coordinates (x, y) Smax = Maximum allowed size of Sxy The adaptive median filter works in two levels denoted Level A and Level B as follows: Level A: A1= Zmed - Zmin A2= Zmed - Zmax If A1 > 0 AND A2 < 0, Go to level B Else increase the window size If window size <=Smax repeat level A Else output Zxy. Level B: B1 = Zxy – Zmin B2 = Zxy – Zmin If B1 > 0 And B2 < 0 output Zxy Else output Zmed. 3.2. Flowchart: Fig-2: Algorithm used for adaptive median filter 4. RESULTS: The standard median filter and adaptive median is been implemented using MATLAB Software. The PSNR (peak signal to noise ratio) is been calculated to understand the efficiency of both the filters. Basically when more is the PSNR value better is noise reduction by the filter. From the calculated values we can observe that the adaptive median filter has better noise filtering capacity than the standard median filter. Table-1: PSNR Value for salt &pepper noise Noise percentage Median Filter (3x3) Median Filter (5x5) Adaptive Median Filter 10% 14.9433 14.625 41.8677 20% 12.009 11.8033 41.8702
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 843 30% 10.2944 10.1596 41.8816 40% 9.1065 8.9761 41.8675 50% 8.0873 8.0011 41.8810 Table-2: PSNR Value for Gaussian noise Noise percentage Median Filter (3x3) Median Filter (5x5) Adaptive Median Filter 10% 19.5683 18.6263 43.2747 20% 19.7813 18.8621 44.4432 30% 20.5014 19.5324 45.4093 40% 21.8413 20.7830 46.0971 50% 23.3442 22.1562 46.5204 Table-3: Output for salt & pepper noise. Table-4: Output for Gaussian noise. CONCLUSIONS After implementing both standard and adaptive median filter, we can observe that the adaptive median filter works accurately for the impulse noise that is salt and pepper noise. For Gaussian noise the adaptive median filter gives much better output than the standard median but we can also observe that the output results are not so satisfactory and accurate ,so there needs to be some more modification to be done in the proposed model to obtained better results for Gaussian noise. Original image Median 3x3 noisy image Median 5x5 Adaptive median filter Original image noisy image median 3x3 Median 5x5 Adaptive median filter
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 10 | Oct 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 844 REFERENCES [1] Rafael C. Gonzalez and Richard E. Woods Digital Image Processing, 2001, pp.220-225. [2] R. Boyle and R. Thomas Computer Vision: A First Course, Blackwell Scientific Publications, 1988, pp. 32 - 34. [3] Suman Shrestha, Image denoising using new adaptive based median filter ,Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.4, August 2014 [4] Uk, Ijeacs. (2017). Performance Assessment of Several Filters for Removing Salt and Pepper Noise, Gaussian Noise, Rayleigh Noise and Uniform Noise. International Journal of Engineering and Applied Computer Science (IJEACS). 02. 176-180. 10.24032/ijeacs/0206/01. [5] Afrose, Zinat. (2012). Relaxed Median Filter: A Better Noise Removal Filter for Compound Images. International Journal on Computer Science and Engineering (IJCSE). [6] Ammayappan, Sathesh. (2018). A Nonlinear Adaptive Median Filtering Based Noise Removal Algorithm.
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