Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

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• 作者：Amarjit Roy ; ^{royamarjit90@gmail.com} ; Rabul Hussain Laskar ^{rabul18@yahoo.com}
• 关键词：Impulse noise ; Non-causal linear prediction ; Adaptive filtering ; Structural similarity
• 刊名：AEU - International Journal of Electronics and Communications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：114-124
• 全文大小：4213 K
• 卷排序：72

文摘

In this paper, a non-causal linear prediction based adaptive vector median filter is proposed for removal of high density impulse noise from color images. Generally, when an image is affected by high density of impulse noise, homogeneity amongst the pixels is distorted. In the proposed method, if the pixel under operation is found to be corrupted, the filtering operation will be carried out. The decision about a particular pixel of being corrupted or not depends on the linear prediction error calculated from the non-causal region around the pixel under operation. If the error of the central pixel of the kernel exceeds some predefined threshold value, adaptive window based vector median filtering operation will be performed. The size of adaptive window will depend on the level of error according to the predefined threshold. The proposed filter improves the peak signal to noise ratio (PSNR) than that of modified histogram based fuzzy filter (MHFC) by approximately 4.5 dB. The results of structural similarity index measure (SSIM) suggest that the image details are maintained significantly better in the proposed method as compared to earlier approaches. It may be observed from subjective evaluation that the proposed method outperforms some of the existing filters.