Multiclass SVM based adaptive filter for removal of high density impulse noise from color images |
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| Affiliation: | 1. School of Computer and Information Science, Southwest University, Chongqing 400715, China;2. Department of Computing, Macquarie University, Sydney, NSW 2109, Australia;3. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;4. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia;5. Corporate Analytics, The Australian Taxation Office, Sydney NSW 2000, Australia;6. Provincial Key Laboratory for Network Based Intelligent Computing, University of Jinan, Jinan 250022, China;1. Department of Chemistry, Graduate School, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea;2. Kohwang Medical Research Institute, School of Medicine, Kyung Hee University, Seoul 130701, Republic of Korea;3. Department of Applied Chemistry and Institute of Natural Sciences, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea;1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China;2. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, PR China;3. School of Mechatronic Engineering and Automation, Shanghai University, 200072, PR China;1. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200240, China;3. Department of Mathematics, Shanghai Jiao Tong University, Shanghai 200240, China;1. Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata 700015, India;2. Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India |
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| Abstract: | This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ∼3 dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods. |
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| Keywords: | Impulse noise Support vector machine Feature vector Adaptive filtering Structural similarity |
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