An efficient LOF-based long-range correlation filter for the restoration of salt and pepper impulse corrupted digital images

An efficient LOF-based long-range correlation filter for the restoration of salt and pepper impulse corrupted digital images

The paper proposes an adaptive long-range correlation-based filter operator for the restoration of impulse corrupted digital images. The impulse detection scheme of the proposed algorithm incorporates the local outlier factor (LOF) to avoid the misclassification of uncorrupted pixels as noise. The restoration algorithm uses the local and remote neighborhood of the same size to find structural similarity among pixels for ensuring better replacement of detected impulses. The domain of the remote window is limited around the neighborhood of the impulsive pixel under concern for maintaining image details and thereby producing a high quality restored image. For replacing impulses, the filter uses a reference image and information about the corruption/purity status of the pixels in the image to determine the most correlated uncorrupted pixel from the remote neighborhood. Experimental results show that the proposed filter is capable of producing better results than the comparative filters in terms of subjective and objective metrics.

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  • [1] Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2008.
  • [2] Ko SJ, Lee YH. Center weighted median filters and their applications to image enhancement. IEEE T Circuits-I 1991; 38: 984-993.
  • [3] Xiang YW, Hong YY, Yu Z, Zhong KF. Image denoising using SVM classification in non sub-sampled contourlet transform domain. Inform Sciences 2013; 246: 155-176.
  • [4] Cai N, Jian C, Jie Y. Applying a wavelet neural network to impulse noise removal. In: IEEE 2005 Neural Networks and Brain Conference; 13–15 October 2005; Media Center Hotel, Beijing, China. New York, NY, USA: IEEE. pp.781-783.
  • [5] Vaseghi SV. Advanced Digital Signal Processing and Noise Reduction. 3rd ed. New York, NY, USA: Wiley, 2008.
  • [6] Singh KM, Bora PK, Singh SB. Rank-ordered mean filter for removal of impulse noise from images. In: IEEE 2002 Industrial Technology Conference; 11–14 December 2002; Bangkok, Thailand. New York, NY, USA: IEEE. pp. 980-985.
  • [7] Sedaaghi MH, Daj R, Khosravi M. Mediated morphological filters. In: IEEE 2001 Image Processing Conference; 7–10 October 2001; Thessaloniki, Greece. New York, NY, USA: IEEE. pp. 692-695.
  • [8] Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE T Circuits-II 1999; 46: 78-80.
  • [9] Indu S, Chaveli R. A noise fading technique for images highly corrupted with impulse noise. In: IEEE 2007 Computing, Theory and Applications Conference; 5–7 March 2007; Kolkata, India. New York, NY, USA: IEEE. pp. 627-632.
  • [10] Nallaperumal K, Varghese J, Saudia S, Arilmozhi K, Velu K, Annam S. Salt & pepper impulse noise removal using adaptive switching median filter. In: IEEE 2006 Oceans Asia Pacific Conference; 16–19 May 2006; Singapore. New York, NY, USA: IEEE. pp. 1-8.
  • [11] How LE, Kai KM. Noise adaptive soft-switching median filter. IEEE T Image Process 2001; 10: 242-251.
  • [12] Fitri U, Keichii U, Gou K. High density impulse noise removal by fuzzy mean linear aliasing window kernel. In: IEEE 2012 Signal Processing, Communication and Computing Conference; 12–15 August 2012; Hong Kong. New York, NY, USA: IEEE. pp. 711-716.
  • [13] Song Y, Yunsang H, Sangkeun L. Pixel correlation-based impulse noise reduction. In: IEEE 2012 Frontiers of Computer Vision Conference; 9–11 February 2011; Ulsan, Korea. New York, NY, USA: IEEE. pp. 1-4.
  • [14] Zhang X, Youlun X. Impulse noise removal using directional difference based noise detector and adaptive weighted mean filter. IEEE Signal Proc Let 2009; 16: 295-298.
  • [15] Mu HH, Fan CC. Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images. Eng Appl Artif Intel 2013; 26: 1331-1338.
  • [16] Shi JH, Ling YH. Using sorted switching median filter to remove high-density impulse noises. J Vis Commun Image R 2013; 24: 956-967.
  • [17] Awad AS. Standard deviation for obtaining the optimal direction in the removal of impulse noise. IEEE Signal Proc Let 2011; 18: 407-410.
  • [18] Jafar I, Al Namneh R, Darabkh K. Efficient improvements on the BDND filtering algorithm for the removal of high density impulse noise. IEEE T Image Process 2013; 22: 1123-1232.
  • [19] Pei EN, Ma KK. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE T Image Process 2006; 15: 1506-1516.
  • [20] Wang W, Peizhong L. An efficient switching median filter based on local outlier factor. IEEE Signal Proc Let 2011; 18: 551-554.
  • [21] Xuming Z, Yi Z, Mingyue D, Wenguang H, Zhouping Y. Decision-based non-local means filter for removing impulse noise from digital images. Signal Process 2013; 93: 517–524.
  • [22] Jie SH. Adaptive salt-&-pepper noise removal: a function level evolution based approach. In: IEEE 2008 Adaptive Hardware and Systems Conference; 22–25 June 2008; Noordwijk, Netherlands. New York, NY, USA: IEEE. pp.391-397.
  • [23] Wang Z, David Z. Restoration of impulse noise corrupted images using long-range correlation. IEEE Signal Proc Let 1998; 5: 4-7.
  • [24] Markus MB, Hans PK, Raymond T, Sander NJ. LOF: identifying density-based local outliers. ACM 2000 Sigmod Record Conference; 16–18 May 2000; New York, NY, USA: ACM. pp. 93-104.
  • [25] Varghese J, Mohamed G, Saudia S, Madappa S, Mohamed SK, Omer BH. An efficient adaptive fuzzy based switching weighted average filter for the restoration of impulse corrupted digital images. IET Image Process 2014; 8: 199-206.
  • [26] Hwang H, Haddad R. Adaptive median filters: new algorithms and results IEEE T Image Process 1995; 4: 499-502.