Abstract
In this paper, an iterative two-stage image denoising technique based on multi-structured textons for noise identification while spatially linked directional similarity for noise restoration (MTNI-SDSNR) is presented for the denoising of random-valued impulse noise (RVIN). Multiple textons oriented at various directions of a sliding window are proposed for the identification of noisy pixels, via an adaptable threshold range computed from their local statistics. Whereas the spatially similar noise-free pixels in the 4 spatially linked directional neighboring pixels obtained by computing the local similarity among them, are used for noise restoration. As the textons are elementary for texture perception, so they are proposed to be oriented based on reflectional symmetry to ensure the effective preservation of salient edges. The proposed MTNI-SDSNR is compared with state-of-the-art denoising methods using standard benchmark grayscale and biomedical images taken from MedPix dataset, by corrupting them with various RVIN intensities. The supremacy of proposed method with similar benchmark RVIN denoising methods can be depicted in quantitative results by showing an increment of 2% (on average) in the values of Peak-signal-to-noise-ratio (PSNR), structural similarity index measurement (SSIM). The visual results represent the edge-preserving capability for both lower and higher intensities of random noise.
Similar content being viewed by others
References
Akkoul S, Ledee R, Leconge R, Harba R (2010) A new adaptive switching median filter. IEEE Signal Process. Lett. 17(6):587–590
Azhar M, Dawood H, Dawood H (2018) Texture-oriented image denoising technique for the removal of random-valued impulse noise. J. Electron. Imaging 27(3):33028
Azhar M, Dawood H, Dawood H, Choudhary GI, Bashir AK, Chauhdary SH (2018) Detail-preserving switching algorithm for the removal of random-valued impulse noise. J. Ambient Intell. Humaniz. Comput.:1–21
Brownrigg DRK (1984) The weighted median filter. Commun ACM 27(8):807–818
Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. in Computer Vision and Pattern Recognition, 2005 CVPR 2005 IEEE Computer Society Conference on 2:60–65
Chen D, Zhu S, Huang Y, Liang J, Chen X (2020) Removal of random-valued impulse noise from Cerenkov luminescence images. Med Biol Eng Comput 58(1):131–141
Chen T and Wu HR, (2001) “Space variant median filters for the restoration of impulse noise corrupted images,” IEEE Trans. Circuits Syst. II Analog Digit Signal Process. 48 (8): 784–789.
Dawood H, Dawood H, Guo P (2015) Removal of random-valued impulse noise by local statistics. Multimed Tools Appl 74(24):11485–11498
Dawood H, Dawood H, and Guo P, (2015) “Removal of random-valued impulse noise by Khalimsky grid,” in Multimedia and Broadcasting (APMediaCast), 2015 Asia Pacific Conference on. 1–6.
Dawood H, Dawood H, Guo P (2017) Generalization of impulse noise removal. Int. Arab J. Inf. Technol
Dawood H et al (2019) Texture-preserving denoising method for the removal of random-valued impulse noise in gray-scale images. Opt. Eng. 58(2):23103
Dong Y, Xu S (2007) A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Process. Lett. 14(3):193–196
Dong Y, Chan RH, Xu S (2007) A detection statistic for random-valued impulse noise. IEEE Trans Image Process 16(4):1112–1120
Garnett R, Huegerich T, Chui C, He W (2005) A universal noise removal algorithm with an impulse detector. IEEE Trans Image Process 14(11):1747–1754
Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11(1):1–21
Habib M, Hussain A, Choi T-S (2015) Adaptive threshold based fuzzy directional filter design using background information. Appl Soft Comput 29:471–478
HosseinKhani Z et al (2018) Adaptive Real-Time Removal of Impulse Noise in Medical Images. J. Med. Syst. 42(11):216
Hussain A, Habib M (2017) A new cluster based adaptive fuzzy switching median filter for impulse noise removal. Multimed Tools Appl 76(21):22001–22018
Hwang H, Haddad RA (1995) Adaptive median filters: new algorithms and results. IEEE Trans Image Process 4(4):499–502
Iqbal N, Ali S, Khan I, Lee BM (2019) Adaptive Edge Preserving Weighted Mean Filter for Removing Random-Valued Impulse Noise. Symmetry (Basel) 11(3):395
Jena B, Patel P, Sinha GR (2018) An efficient random valued impulse noise suppression technique using artificial neural network and non-local mean filter. Int J Rough Sets Data Anal 5(2):148–163
Jiang J, Zhang L, Yang J (2014) Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Trans Image Process 23(6):2651–2662
** Q, Bai L, Grama I, Liu Q, Yang J (2020) Removing random-valued impulse noise with reliable weight. Inverse Probl. Imaging 14(2):171
Julesz B (1981) Textons, the elements of texture perception, and their interactions. Nature 290(5802):91–97
Julesz B (1986) Texton gradients: the texton theory revisited. Biol Cybern 54(4–5):245–251
Kang M, Kang M, Jung M (2019) Sparse representation based image deblurring model under random-valued impulse noise. Multidimens Syst Signal Process 30(3):1063–1092
Karthik B, Kumar TK, Vijayaragavan SP, 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
Kumar S, Sen Yadav J, Kurmi Y, and Baronia A (2020) “An efficient image denoising approach to remove random valued impulse noise by truncating data inside sliding window,” in 2nd International Conference on Data, Engineering and Applications (IDEA) pp. 1–7.
Li G, Xu X, Zhang M, Liu Q (2020) Densely connected network for impulse noise removal. Pattern Anal. Appl.:1–13
Lin C, Li Y, Feng S, Huang M (2020) A two-stage algorithm for the detection and removal of random-valued impulse noise based on local similarity. IEEE Access 8:222001–222012
Lu C-T, Chen M-Y, Shen J-H, Wang L-L, Yen NY, Liu C-H (2018) X-ray bio-image denoising using directional-weighted-mean filtering and block matching approach. J. Ambient Intell. Humaniz. Comput.:1–18
Ma C, Lv X, Ao J (2019) Difference based median filter for removal of random value impulse noise in images. Multimed Tools Appl 78(1):1131–1148
Marudhachalam R, Selvanayaki S, Tamilselvi R, and Devaki P, (2020) “Directional weighted hybrid median based fuzzy filter for de-noising random valued impulse noise,” in AIP Conference Proceedings, 2270(1):140010.
Murugan K, Arunachalam VP, Karthik S (2018) A hybird filtering approach for mri image with multiresolution. Cluster Comput.:1–6
Nadeem M, Hussain A, Munir A, Habib M, and Naseem MT, (2019) “Removal of Random Valued Impulse Noise from Grayscale images using Quadrant based Spatially Adaptive Fuzzy Filter,” Signal Processing, p. 107403, doi: https://doi.org/10.1016/j.sigpro.2019.107403.
Nadeem M, Hussain A, Munir A, Habib M, Naseem MT (2020) Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter. Signal Process 169:107403
Patel P and Jena B (2021) “Neural Network-Based Random-Valued Impulsive Noise Suppression Scheme,” in Intelligent System Design, Springer, pp. 759–769.
Pitas I, Venetsanopoulos AN (1992) Order statistics in digital image processing. Proc. IEEE 80(12):1893–1921
Rao GS, Kumari GV and Rao BP (2018) “New Random Noise Denoising Method for Biomedical Image Processing Applications,” in International Conference on ISMAC in Computational Vision and Bio-Engineering, pp. 355–365.
Soleimany S and Hamghalam M, (2017) “A novel random-valued impulse noise detector based on MLP neural network classifier,” in Artificial Intelligence and Robotics (IRANOPEN), pp. 165–169.
Sun T, Neuvo Y (1994) Detail-preserving median based filters in image processing. Pattern Recognit. Lett. 15(4):341–347
Turkmen I (2013) A new method to remove random-valued impulse noise in images. AEU-Int J Electron Commun 67(9):771–779
Turkmen I (2016) The ANN based detector to remove random-valued impulse noise in images. J Vis Commun Image Represent 34:28–36
Uddin Khan N, Arya KV (2020) A new fuzzy rule based pixel organization scheme for optimal edge detection and impulse noise removal. Multimed. Tools Appl.:1–27
Veerakumar T, Subudhi BN, Esakkirajan S (2019) Empirical mode decomposition and adaptive bilateral filter approach for impulse noise removal. Expert Syst Appl 121:18–27
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
Wu Y, Tracey BH, Natarajan P, Noonan JP (2014) Fast blockwise SURE shrinkage for image denoising. Signal Process 103:45–59
**ong B, Yin Z (2012) A universal denoising framework with a new impulse detector and nonlocal means. IEEE Trans Image Process 21(4):1663–1675
Xu S, Zhang G, Hu L, Liu T (2005) Convolutional neural network-based detector for random-valued impulse noise. J. Electron. Imaging 27(5):50501
Xu Y, Liu H, Cheng Z (2011) Harnessing the power of radionuclides for optical imaging: Cerenkov luminescence imaging. J Nucl Med 52(12):2009–2018
Availability of data and material (data transparency)
The Data will be available on request.
Code availability (software application or custom code)
The Code will be available on request.
Funding
The work was funded by the University of Jeddah, Saudi Arabia under Grant No (UJ-20-054-DR). The authors, therefore, acknowledge with thanks the university’s technical and financial support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest/competing interests
The Author(s) declare(s) that there is 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
About this article
Cite this article
Dawood, H., Daud, A., Dawood, H. et al. Reduction of random-valued impulse noise by using multi-structured textons. Multimed Tools Appl 81, 15303–15331 (2022). https://doi.org/10.1007/s11042-022-12578-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12578-9