Image Processing using Universal Filter

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 05, 2016 | ISSN (online): 2321-0613

Image Processing using Universal Filter Sharanjit Singh1 Simmy2 Assistant Professor 2M.Tech. Student 1,2 Department of Computer Science & Engineering 1,2 G.N.D.U Regional Campus, Gurdaspur 1

Abstract— The image processing is the mechanism of clearing any noise while preserving the quality of the image.

The noise can be caused by the temperature rise or due to the medium from which the information is transferred. The Gaussian noise is another kind of noise which has same probability density function as the normal function. The proposed paper deals with design of universal filter which tackles salt and pepper as well as Gaussian nose. The experimental evaluation shows that the proposed filtering produced better result as compared to the median and homomorphic filter. The universal filter is proposed which will takes into consideration multiple noises within the image and rectify it. The existing filters deals with only certain type of noises hence for distinct noise distinct filter is required. The universal filter solves this problem and reduces the time and complexity associated with the filtering mechanism. Our approach is extended to medical images to automatically remove mix noise in terms of Gaussian and Impulse noise.

Key words: Impulse Noise, Gaussian Noise, Universal Filter, Mix Noise. Medical Images I. INTRODUCTION The image processing provides graphical representation of object to be represented. The interface which is provided is known as GUI. The Graphical user interface will present the information in a manner which is easy to understand. The area such as medical line uses such image processing techniques in order to analyse the abnormal behaviour like diseases, of the image concerned. [1] State-of-the-art schemes for reversible data hiding (RDH) usually consist of two steps: first construct a host sequence with a sharp histogram via prediction errors, and then embed messages by modifying the histogram with methods, such as difference expansion and histogram shift. In this paper, we focus on the second stage, and propose a histogram modification method for RDH, which embeds the message by recursively utilizing the decompression and compression processes of an entropy coder. We prove that, for independent identically distributed (i.i.d.) gray-scale host signals, the proposed method asymptotically approaches the rate-distortion bound of RDH as long as perfect compression can be realized, i.e., the entropy coder can approach entropy. Therefore, this method establishes the equivalency between reversible data hiding and lossless data compression. Experiments show that this coding method can be used to improve the performance of previous RDH schemes and the improvements are more significant for larger images. The image presented has to be converted into grey scale in order to perform the operation of histogram equivalence.

The grey scale images are used for better observing the problems present within the images. The grey scale images are converted back to original form by the use of database which is used in order to encode the coloured

images. The image processing will require techniques in order to enhance the image for introducing clarity in the analysis process. During the analysis process pixel values are going to be analysed. Pixel is phosphorus dot which will glow when the electron beam strikes on it. The pixel will have horizontal and vertical position associated with it. The intensity values will be disturbed when the noise is introduced within the image.

The intensity hike in the image will cause the distortion which will be handled by the use of histogram equivalence process. The implementation of the proposed system is done by use of tool known as MATLAB. The digital image processing tools are provided within the MATLAB for handling noise present within the image.

The two phase mechanism is used to handle noise present within the image. The noise considered in the proposed system is categorized as A. Impulse Noise The impulse noise is caused by rise in temperature within the image. The pixels are phosphorous dots which will be exited with the rise in temperature. The exited pixels when comes to ground state emits energy in the form of light and distortion appears within the image. The image affected by the impulse noise has least clarity.

Fig. 1(a): Showing Image distorted with Impulse Noise Fig 1(b) Showing Original Image B. Gaussian Noise It is a statistical noise whose probability distribution function is similar to original distribution. This will cause overlapping pixels and hence cause distortion within the image.

Fig. 2(a): Showing Original Image Fig 2(b): Showing Gaussian Noise

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Image Processing using Universal Filter (IJSRD/Vol. 4/Issue 05/2016/212)

In order to tackle such distortions median filter and homomorphic filters are present but these filters have to be used individually. This process is time consuming. So we propose universal filter which automatically detect and restore the image to its original state.

II. RELATED WORK The Gaussian noise will be introduced within the image due to corrupt medium through which image is made to transfer.

[2] This paper addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively.

Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior those forces sparsely over patches in every location in the image. This shows how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods. [3] Salt and pepper noise restoration process is suggested in this paper. The median filter is used to achieve this restoration. The restoration mechanism is efficient since buffer is used in order to store the pixel values. The image will be enhanced by replacing the existing corrupted pixels by the normal pixels stored within the image. [4] B Splines mechanism is considered in this paper to reduce the Salt and pepper noise present within the image. The Abnormal Spline curves are produced when distortion is present. By analysing the abnormal effect the noise is removed from the image. [5] Noise from the image is reduced by the use of histogram mechanism. The noise levels in the analogy to digital conversion process is analysed in this case. [2], [6]–[8] the image denoising mechanism in terms of Gaussian and salt and pepper noise is considered in the suggested paper. The image denoising can be removed by the use of median filter where corrupted pixels are restored by replacing the pixels with its neighbouring pixels.

The noise present within the image is the problem which must be handled in order to analyze the image in practical situations. The most common type of error which is present within the image is Gaussian and salt and pepper noise. The problem is partially solved by the use of median filter but the problem start to occur when temperature increases beyond certain level. In order to solve the problem Universal filter is suggested.

III. PROPOSED SYSTEM The proposed system takes into consideration both the noises salt and pepper also known as impulse noise and Gaussian noise.[3], [7], [9]–[12] The filter which we are proposing is universal filter which takes into consideration both the noise and rectify and restore the images in efficient manner.

The image has impulse noise within the image if pixel intensity value reaches 0 or 255. The level of intensity

values has to be minimized to average values of the intensities to control the impulse noise from the image. In order to determine the impulse noise from the image intensity values from the image are analysed.

[3], [4], [6], [7], [9], [11], [13]–[17] The image can also contain the Gaussian noise. The Gaussian noise from the image will be detected by the use of probability density functions. The probability function has same values as the normal distribution. By analysing this factor this noise can be detected.

The Universal filter which is used has the following algorithm UF(Image) 1) Take the input image and store it in buffer 2) X=imread(image) 3) Analyse the pixel intensity values 4) If(Intensity(X)==0 or 255) 5) Image is distorted and apply Impulse noise detection mechanism 6) End of if 7) If(PDF(image)==Normal(Image) 8) Gaussian noise is present and apply Gaussian noise handling mechanism 9) End of if The flow diagram showing the working of Universal filter is described as under

Fig. 3: Diagram Showing the working of Universal filter The salt and pepper noise handling mechanism is implemented using the pixel comparison and restoring mechanism. đ?›˝ đ??šđ?‘Ś (đ?‘˘) = ∑| đ?‘˘đ?‘– đ?‘— − đ?‘Śđ?‘– đ?‘— | + âˆ‘âˆ‘âˆł |đ?‘˘đ?‘– đ?‘— − đ?‘˘đ?‘š đ?‘› | 2 Where y defines the combination of four closest pixels. It is shown that mild values of đ?›˝ substituted in the above equation give restored pixel.

The Gaussian noise handling mechanism introduced in the proposed system is described through following equation (đ?‘§âˆ’đ?‘˘)2 1 − đ?‘ƒđ??şđ?‘§ (đ?‘˘) = đ?‘’ 2đ?œŽ2 đ?œŽâˆš2đ?œ‹

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Image Processing using Universal Filter (IJSRD/Vol. 4/Issue 05/2016/212)

The above equation will give the probability density function of the normal distribution. Its value can be compared against the infected image probability density function. The difference can be eliminated in order to restore the image.

IV. SIMULATION AND RESULTS The simulation of the universal filter is accomplished within the MATLAB tool. The MATLAB is a mathematical tool in which digital image processing can be handled efficiently.

The Simulation results shows that clarity of the image is introduced using the proposed mechanism. The image processing toolbox is present within the simulator which is used in order to enhance the image which is present to the system. MATLAB utilizes mathematical tool in order to enhance the intensity and contrast within the image. The Simulation results shows that clarity of the image is introduced using the proposed mechanism.

Entropy Noise Brightness error 4.988 0.1 4.322 3.222 0.2 2.345 Existing 5.678 0.2 3.456 Work 5.654 0.1 4.443 4.567 0.1 4.678 5.632 0.2 3.456 Table 1: Showing the distinct parameter values obtained from existing system In terms of charts the values are plotted as

Fig. 6: chart of distinct parameter values obtained The proposed method with universal filter is given through following tabular structure as Entropy Noise Brightness Error 6.234 0.1 2.332 4.345 0.1 1.234 Proposed 6.667 0.1 2.000 Work 6.789 0.1 2.345 7.234 0.1 2.123 6.765 0.1 1.987 Table 2: Showing the proposed system parameter values Fig. 4: Showing the restoration of image from impulse and Gaussian Noise

Fig. 7: chart of proposed system parameter values The result in terms of charts suggest that the proposed system is optimal and yielding optimal values Fig. 5: Showing the restoration of image from impulse and Gaussian Noise The first image is corrupted with impulse and Gaussian noise. When the universal filter is applied the image is improved by eliminating salt and pepper noise from the image. In the next phase Gaussian noise is also eliminated and image is enhanced.

V. PERFORMANCE ANALYSIS The performance analysis indicates the existing system has more mean brightness error and least entropy. This is improved using the proposed system. The tables shows the reading taken after running the simulation as

VI. CONCLUSION AND FUTURE WORK The proposed model combines the use of multiple filters in order to form universal filter . The technique can be used in order to eliminate the salt and pepper and Gaussian noise from within the image. The filter produced better result as compared to individual use of median and homomorphic filter. The universal filter will automatically identify the noise and apply the noise handling mechanism accordingly.

In case both the noises are corrupting the image then both noises are automatically handled in the present situation.

In the future we try to accommodate more noise handling mechanisms within the universal filter so that single filter can be used to tackle all the noises present within the image.

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REFERENCES [1] W. Zhang, X. Hu, X. Li, and N. Yu, “Recursive histogram modification: establishing equivalency between reversible data hiding and lossless data compression.,” IEEE Trans. Image Process., vol. 22, no.

7, pp. 2775–85, Jul. 2013.

[2] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries.,” IEEE Trans. Image Process., vol. 15, no.

12, pp. 3736–45, 2006.

[3] Y. Zhang, Y. Liu, X. Li, and C. Zhang, “Salt and pepper noise removal in surveillance video based on low-rank matrix recovery,” Comput. Vis. Media, vol. 1, no. 1, pp.

59–68, Aug. 2015.

[4] P. S. Jayasree, P. Raj, P. Kumar, R. Siddavatam, and S. P. Ghrera, “A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines,” Signal, Image Video Process., vol. 7, no. 6, pp. 1145– 1157, Aug. 2012.

[5] P. Carbone and D. Petri, “Noise sensitivity of the ADC histogram test,” in IMTC/98 Conference Proceedings.

IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222), vol. 1, pp. 88–91.

[6] A. H. Pilevar, S. Saien, M. Khandel, and B. Mansoori, “A new filter to remove salt and pepper noise in color images,” Signal, Image Video Process., vol. 9, no. 4, pp. 779–786, Jul. 2013.

[7] E. J. Leavline, D. A. Antony, and G. Singh, “Salt and Pepper Noise Detection and Removal in Gray Scale Images : An Experimental Analysis,” vol. 6, no. 5, pp.

343–352, 2013.

[8] D. Redundancy, “Chapter 2 Digital Image Compression,” pp. 4–15.

[9] P. S. J. Sree, P. Kumar, R. Siddavatam, and R. Verma, “Salt-and-pepper noise removal by adaptive medianbased lifting filter using second-generation wavelets,” Signal, Image Video Process., vol. 7, no. 1, pp. 111– 118, Feb. 2011.

[10] P. B. Tambe, P. D. Kulhare, M. D. Nirmal, and P. G. Prajapati, “Image Processing ( IP ) Through Erosion and Dilation Methods,” vol. 3, no. 7, pp. 285–289, 2013.

[11] I. H. Witten, A. Moffat, and T. C. Bell, Compressing and Indexing Documents and Images. .

[12] F. Khalvati, “Computational Redundancy in Image Processing,” Image (Rochester, N.Y.), no. November, 2008.

[13] X. Zhang, F. Ding, Z. Tang, and C. Yu, “Salt and pepper noise removal with image inpainting,” AEU Int. J. Electron. Commun., vol. 69, no. 1, pp. 307–313, Jan. 2015.

[14] K. J. Patil, M. Z. Chopda, and R. T. Mahajan, “Lipase biodiversity,” Indian J. Sci. Technol., vol. 4, no. 8, pp.

971–982, 2011.

[15] “Solution to DDoS issues for streamers and players.” .

[16] B. Bandyopadhyay, I. Bhattacharyya, S. Adhikary, J. Konar, N. Dawar, J. Sarkar, S. Mondal, M. S. Chauhan, N. Bhattacharya, A. Chakravarty, A. Biswas, and B. Saha, “A Comprehensive Study on the 2012 Dengue Fever Outbreak in Kolkata , India,” vol. 2013. 2013.

[17] E. The, “Chapter 2,” pp. 32–54.

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