Abstract
Impulse noise is a challenging problem that degrades the quality of an image. In last few decades, Median filtering denoising method has been widely used for impulse noise. Several well-known and efficient algorithms and techniques exist to effectively remove either Gaussian noise or Impulse noise, independently However, in order to remove high noise densities, there has been a shift in the process of filtering methods. New methods adopted are usually computationally expensive. In this paper, Nearest Neighbour median Filter method has been proposed for impulse noise reduction. The proposed method exploited correlation between the pixels of an image. The main objective of proposed approach is detection and reduction of impulse noise in corrupted images without any loss of information. The performance of proposed denoising technique is compared with existing methods on the basis of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Virtual Information Fidelity (VIF) and computational complexity. From the experimental analysis, it is evident that the proposed denoising method removes impulse noise very effectively, especially at higher noise density levels (more than 70%). Moreover, computational complexity of proposed approach is lesser as compared to state-of-the art methods.
Graphical abstract
Graphical abstract of Nearest Neighbour median Filter
Similar content being viewed by others
References
Petrou M, Petrou C (2010) Image Processing: the fundamentals. John Wiley & Sons, Singapore
Toh KKV, Isa NAM (2009) Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process Lett 17(3):281–284
Erkan U, Gökrem L (2018) A new method based on pixel density in salt and pepper noise removal. Turkish J Electr Eng Comput Sci 26(1):162–171
Kandemir C, Kalyoncu C, Toygar Ö (2015) A weighted mean filter with spatial-bias elimination for impulse noise removal. Digit Signal Proc 46:164–174
Kim DG, Hussain M, Adnan M, Farooq MA, Shamsi ZH, Mushtaq A (2020) Mixed noise removal using adaptive median based non-local rank minimization. IEEE Access 9:6438–6452
Ng PE, Ma KK (2006) A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans Image Process 15(6):1506–1516
Mafi M, Rajaei H, Cabrerizo M, Adjouadi M (2018) A robust edge detection approach in the presence of high impulse noise intensity through switching adaptive median and fixed weighted mean filtering. IEEE Trans Image Process 27(11):5475–5490
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: 2010 IEEE Second International Conference on Computer Modeling and Simulation (Vol. 4, pp 409–413)
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
Singh A, Sethi G, Kalra GS (2020) Spatially adaptive image denoising via enhanced noise detection method for grayscale and color images. IEEE Access 8:112985–113002
Karthik B, Kumar K, Vijayaragavan T, Sriram M (2021) Removal of high-density salt and pepper noise in color image through modified cascaded filter. J Ambient Intell Humaniz Comput 12(3):3901–3908
Senthil Selvi A, Sukumar R (2019) Removal of salt and pepper noise from images using hybrid filter (HF) and fuzzy logic noise detector (FLND). Concurr Comput: Pract Exper 31(12):e4501
Hsieh MH, Cheng FC, Shie MC, Ruan SJ (2013) Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images. Eng Appl Artif Intell 26(4):1333–1338
Wang Z, Zhang D (1999) Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE Trans Circuits Syst II: Analog Digit Signal Process 46(1):78–80
Kumar N, Dahiya AK, Kumar K (2020) Modified median filter for image denoising. Int J Adv Sci Technol 29:1495–1502
Thanh DNH, Engínoğlu S (2019) An iterative mean filter for image denoising. IEEE Access 7:167847–167859
Erkan U, Gökrem L, Enginoğlu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798
Chen T, Wu HR (2001) Adaptive impulse detection using center-weighted median filters. IEEE Signal Process Lett 8(1):1–3
Lee CS, Kuo YH (2000) Adaptive fuzzy filter and its application to image enhancement. Fuzzy techniques in image processing. Physica, Heidelberg, pp 172–193
Wang JH, Liu WJ, Lin LD (2002) Histogram-based fuzzy filter for image restoration. IEEE Trans Syst Man Cybern Part B (Cybernetics) 32(2):230–238
Schulte S, Nachtegael M, De Witte V, Van der Weken D, Kerre EE (2006) A fuzzy impulse noise detection and reduction method. IEEE Trans Image Process 15(5):1153–1162
Lee CS, Guo SM, Hsu CY (2005) Genetic-based fuzzy image filter and its application to image processing. IEEE Trans Syst Man Cybernetics Part B (Cybernetics) 35(4):694–711
Hussain A, Jaffar MA, Siddiqui AB, Nazir M, Mirza AM (2009) May Modified histogram based fuzzy filter. International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications. Springer, Berlin, Heidelberg, pp 277–284
Enginoğlu S, Erkan U, Memiş S (2019) Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal. Multimed Tools Appl 78(24):35401–35418
Thanh DN, Hien NN, Kalavathi P, Prasath VS (2020) Adaptive switching weight mean filter for salt and pepper image denoising. Procedia Comput Sci 171:292–301
Wang Y, Wang J, Song X, Han L (2016) An efficient adaptive fuzzy switching weighted mean filter for salt-and-pepper noise removal. IEEE Signal Process Lett 23(11):1582–1586
Khan KB, Shahid M, Ullah H, Rehman E, Khan MM (2018) Adaptive trimmed mean autoregressive model for reduction of Poisson noise in scintigraphic images. IIUM Eng J 19(2):68–79
Leng K (2017) An improved non-local means algorithm for image denoising. In: 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), pp 149–153
Singh D, Kaur A (2021) Fuzzy based fast non local mean filter to denoise Rician noise. Mater Today: Proc 46:6445–6452
Jaiswal A, Upadhyay J, Somkuwar A (2014) Image denoising and quality measurements by using filtering and wavelet-based techniques. AEU-Int J Electron Commun 68(8):699–705
Amiri Golilarz N, Gao H, Kumar R, Ali L, Fu Y, Li C (2020) Adaptive wavelet-based MRI brain image de-noising. Front NeuroSci 14:728
Jain P, Tyagi V (2015) LAPB: locally adaptive patch-based wavelet domain edge-preserving image denoising. Inf Sci 294:164–181
Jing-Yi L, Hong L, Dong Y, Yan-Sheng Z (2016) A new wavelet threshold function and denoising application. Math Probl Eng 2016:1–9
Lone MR, Khan E (2022) A good neighbor is a great blessing: nearest neighbor filtering method to remove impulse noise. J King Saud Univ - Comput Inf Sci 34(10):9942–9952
Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. City 1(2):1
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Seshadrinathan K, Bovik AC (2008) Unifying analysis of full reference image quality assessment. In: 2008 15th IEEE International Conference on Image Processing, pp 1200–1203
Esakkirajan S, Veerakumar T, Subramanyam AN, PremChand CH (2011) Removal of high-density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Process Lett 18(5):287–290
Hussain A, Bhatti M, Jaffar MA (2012) Fuzzy based impulse noise reduction method. Multimed Tools Appl 60(3):551–571
Lim JS (1990) Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall, pp 710–720
Funding
No funding provided by any Authority.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lone, M.R., Sandhu, A.K. Enhancing image quality: A nearest neighbor median filter approach for impulse noise reduction. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17693-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11042-023-17693-9