Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

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• 作者：Amarjit Roy ; ^{royamarjit90@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Rabul Hussain Laskar ^{rabul18@yahoo.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Impulse noise ; Support vector machine ; Feature vector ; Adaptive filtering ; Structural similarity
• 刊名：Applied Soft Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：46
• 期：Complete
• 页码：816-826
• 全文大小：3926 K

文摘

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.