An Adaptive Content based Closer Proximity Pixel Replacement algorithm for the removal of high density salt and pepper noise in images is proposed. The algorithm uses decision tree to identify and correct the pixels of the image is noisy or not. The algorithm finds Euclidean distance between the processed pixel and the number of non-noisy pixels inside the current processing kernel. The algorithm requires only two non-noisy pixels to be present in kernel for the algorithm to operate. The faulty pixels are replaced only by the median of pixels that occurs more frequently in the current processing kernel based on the Euclidean distance. The algorithm increases the window size by two when there are no non-noisy pixels in the current processing kernel. The proposed algorithm was compared with 16 standard and existing algorithms derived from recent literatures. Exhaustive experiments on standard database images suggest that the algorithm exhibit excellent noise suppression and good information preservation characteristics even at very high noise densities.
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Salt and pepper noise
Adaptive decision based kriging interpolation filter
Peak signal to noise ratio
Mean square error
Image enhancement factor
Structural Similarity Index metric
Figure of merit
Standard median filter
Adaptive median filter
Center weighted filter
Alpha trimmed mean filter
Progressive switched median filter
Modified decision based median filter
Decision based median algorithm
Improved decision based median filter
Cascaded unsymmetrical trimmed mean filter
Cascaded unsymmetrical decision based midpoint filter
Modified decision based unsymmetrical trimmed median filter
Modified decision based unsymmetrical trimmed median filter with global mean
Adaptive Cardinal B Spline interpolation filter
Noise adaptive fuzzy switched median
Adaptive weighted mean filter
Cumulative probability blur detection metric
Normalized correlation coefficient
Abdou IA, Pratt W (1979) Quantitative design and evaluation of enhancement/thresholding edge detectors. Proc IEEE 67(5):753–766
Aiswarya K, Jayaraj V, Ebenezer D (2010) A New and efficient Algorithm for the removal of high density salt and pepper noise in images and videos. In: Proceedings in International conference on computer modelling and simulation, pp. 409–413.
Azhar M, Dawood H, Dawood H, Choudhary GI, Bashir AK, Chauhdary SH (2019) Detail-preserving switching algorithm for the removal of random-valued impulse noise. J Ambient Intell Human Comput 10:3925–3945. https://doi.org/10.1007/s12652-018-1153-0
Bai T, Tan J, Min H, Yan W (2012) A novel algorithm for removal of salt and pepper noise using continued fractions interpolation. J Signal Process 102:247–255
Balasubramanian S, Kalishwaran S, Muthuraj R, Ebenezer D, Jayaraj V (2009) An efficient Non linear cascade filtering algorithm for removal of high density salt and pepper noise in image and video sequence. In: Proceedings in International Conference on control, Automation, communication and Energy Conservation, pp. 1–6.
Brownrigg DRK (1984) The weighted median filter. Commun ACM 27(8):807–818
Chan RH, Ho C-W, Nikolova M (2005) Salt and pepper noise removal by median –type noise detectors and detail preserving regularization. IEEE Trans Image Process 14(10):1479–1485
Chen T, Ma KK, Chen LH (1999) Tri-state median filter for image denoising. IEEE Trans Image Process 8(12):1834–1838
Cumpim C, Punchalard R, Janchitrapongvej K, Kimpan C (2016) Salt and pepper noise removing by Sheperd interpolation method. In: 13th International Conference on Electrical Engineering Electronics, Computer, Telecommunications and Information Technology, pp. 1–8.
Eng H, Ma K (2001) Noise adaptive soft-switching median filter. IEEE Trans Image Process 10(2):242–251
Esakkirajan S, Veerakumar T, Subramanyam AN, Prem Chand CH (2011) Removal of high density Salt and pepper noise through modified decision based unsymmetrical trimmed median filter. IEEE Signal Process Lett 5:287–290
Huang T, Yang GJ (1979) A fast two-dimensional median filtering algorithm. IEEE Trans Acoust Speech Signal Process 27(1):13–18
Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4:499–502
Karthik B, Kumar TK, Vijayaragavan SP, Sriram M (2020) Removal of high density salt and pepper noise in color image through modified cascaded filter. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01737-1
Ko SJ, Lee YH (1991) Center weighted median filters and their application to image enhancement. IEEE Trans Circuits Syst 38(9):984–993
Madhu S, Nair RK, Tatavarti R (2008) An improved decision based algorithm for impulse noise removal. In: Proceedings in Congress on Image and Signal Processing, pp. 426–431
Pahlavani F, Nabaee M (2014) Linear surface interpolation for salt and pepper noise removal. Int Conf Comput Knowl Eng. https://doi.org/10.1109/ICCKE.2014.6993447
Peixuan Z, Fang L (2014) A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE Signal Process Lett 21(10):1280–1283
Pitas I, Venetasanpoulos AN (1990) Non linear digital filters: principles and applications. Springer, Boston
Srinivasan KS, Ebenezer D (2007) A new fast and efficient decision-based algorithm for removal of high-density impulse noises. IEEE Signal Process Lett 14(3):189–192
Syamala PJ, Raj P, Kumar P, Siddavatam R, Ghrera SP (2013) A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines. Signal Image Video Process 7(6):1145–1157
Tena J, Vasanth K, Govindaswamy I (2014) An enhanced decision based algorithm for the reduction of high density salt and pepper noise with reduced streak. In: Proceedings in International Conference on Electronics and Communication Systems (ICECS-2014), pp. 1–8.
Varatharajan R, Vasanth K, Manogaran G, Priyan M, Zao XZ (2017) An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.05.035
Vasanth K, Jawahar VK (2014) Decision-based neighborhood-referred unsymmetrical trimmed variants filter for the removal of high-density salt-and-pepper noise in images and videos. J Signal Image Video Process. https://doi.org/10.1007/s11760-014-0665-0
Vasanth K, Packyanathan G, Kamatham H et al (2017) An adaptive decision based interpolation scheme for the removal of high density salt and pepper noise in images. J Image Video Proc 2017:67. https://doi.org/10.1186/s13640-017-0215-0
Veerakumar T, Esakkirajan S, Ila V (2012) An approach to minimize very high density salt and pepper noise through trimmed global mean. Int J Comput Appl 39(12):28–33
Veerakumar T, Esakkirajan S, Ila V (2014) Recursive cubic spline interpolation filter approach for the removal of high density salt-and-pepper noise. International Journal of Signal Image and Video Processing, Springer, Berlin, pp 159–168
Veerakumar T, Jagannath RP, Subudhi BN, Esakkirajan S (2017) Impulse noise removal using adaptive radial basis function interpolation. Circuits Syst Signal Process 36(3):1192–1223
Wang Z and Zhang D (1999) Progressive switching median filter for removal of impulse noise from highly corrupted images. IEEE Transactions on Circuits Systems-II, pp. 78–80. https://sipi.usc.edu/database/ Accessed 13 May 2020.
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error measurement to structural similarity. IEEE Trans Image Process 13(4):102–109
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Vasanth, K., Varatharajan, R. An adaptive content based closer proximity pixel replacement algorithm for high density salt and pepper noise removal in images. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02376-2
- Salt and pepper noise
- Euclidean distance