A Survey on Different Noise Removal Techniques in Images

S. Eljin*
Post Graduate, Department of Applied Electronics, C.S.I Institute of Technology, Thovalai, India.
Periodicity:October - December'2017
DOI : https://doi.org/10.26634/jdp.5.4.14563

Abstract

The process of removing noise from the signal is known as Noise Reduction. Both the digital and analog recordable devices can be affected by noise. Noise can be of two variations, it can be either a coherent noise which could be introduced by the algorithm or they can be of non-coherent with white or random noise. Since the structure of the medium is a grained one, noise is introduced in both the photographic and magnetic taped scenarios accordingly. Noise can be reduced by different techniques with a corresponding algorithm or methodology, whereas in this paper, the author comprises the survey of different noise removal techniques from different authors’ point of view.

Keywords

Noise Removal, Digital Images, Signal Processing.

How to Cite this Article?

Eljin, S. (2017). A Survey on Different Noise Removal Techniques in Images. i-manager's Journal on Digital Signal Processing, 5(4), 27-33. https://doi.org/10.26634/jdp.5.4.14563

References

[1]. Abreu, E., & Mitra, S. K. (1995). A signal-dependent rank ordered mean (SD-ROM) filter - A new approach for removal of impulses from highly corrupted images. Proc. Int. Conf. Acoust. Speech Signal Processing, 4, 2371-2374.
[2]. Abreu, E., Lightstone, M., Mitra, S. K., & Arakawa, K. (1996). A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Transactions on Image Processing, 5(6), 1012-1025.
[3]. Baghaie, A., Dā€˜souza, R. M., & Yu, Z. (2015, April). Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT images. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on (pp. 226-230). IEEE.
[4]. Burock, M. A., &Dale , A. M. (2000). Estimation and detection of event-related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach. Human Brain Mapping, 11(4), 249-260.
[5]. Cheng, Q., Shen, H., Zhang, L., Yuan, Q., & Zeng, C. (2014). Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 54-68.
[6]. Chou, H. H., Hsu, L. Y., & Hu, H. T. (2013). Turbulent-PSObased fuzzy image filter with no-reference measures for high-density impulse noise. IEEE Transactions on Cybernetics, 43(1), 296-307.
[7]. Esakkirajan, S., Veerakumar, T., Subramanyam, A. N., & PremChand, C. H. (2011). Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. IEEE Signal Processing Letters, 18(5), 287-290.
[8]. Fabijanska, A., & Sankowski, D. (2011). Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images. IET Image Processing, 5(5), 472-480.
[9]. Helen, R., Kamaraj, N., Selvi, K., & Raman, V. R. (2011, March). Segmentation of pulmonary parenchyma in CT lung images based on 2D Otsu optimized by PSO. In Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on (pp. 536- 541). IEEE.
[10]. Hosseini, H., & Marvasti, F. (2013). Fast restoration of natural images corrupted by high-density impulse noise. EURASIP Journal on Image and Video Processing, 2013(1), 15-21.
[11]. Huanping, Z., Yan, L., & Huojie, S. (2017). Analysis on digital image Re-Ranking Algorithm based on Multi-feature fusion. Journal of Engineering Science & Technology Review, 10(2), 42-50.
[12]. John, N., Anusha, B., & Kutty, K. (2015). A reliable method for detecting road regions from a single image based on color distribution and vanishing point location. Procedia Computer Science, 58, 2-9.
[13]. Kailath, T. (1971). RKHS approach to detection and estimation problems - I: Deterministic signals in Gaussian noise. IEEE Transactions on Information Theory, 17(5), 530- 549.
[14]. Kim, V., & Iaroslavskii, L. (1986). Rank algorithms for picture processing. Computer Vision Graphics and Image Processing, 35, 234-258.
[15]. Lahmiri, S., & Boukadoum, M. (2016, February). Combined partial differential equation filtering and particle swarm optimization for noisy biomedical image  segmentation. In Circuits & Systems (LASCAS), 2016 IEEE 7th Latin American Symposium on (pp. 363-366). IEEE.
[16]. Lee, K. C., Song, H. J., & Sohn, K. H. (1998). Detectionestimation based approach for impulsive noise removal. Electronics Letters, 34(5), 449-450.
[17]. Pan, J., Yang, X., Cai, H., & Mu, B. (2016). Image noise smoothing using a modified Kalman filter. Neurocomputing, 173, 1625-1629.
[18]. Peng, S., & Lucke, L. (1995, April). Multi-level adaptive fuzzy filter for mixed noise removal. In Circuits and Systems, 1995. ISCAS'95., 1995 IEEE International Symposium on (Vol. 2, pp. 1524-1527). IEEE.
[19]. Roomi, S. M. M., & Rajee, R. J. (2011, June). Speckle noise removal in ultrasound images using Particle Swarm Optimization technique. In Recent Trends in Information Technology (ICRTIT), 2011 International Conference on (pp. 926-931). IEEE.
[20]. Smolka, B., Malik, K., & Malik, D. (2015). Adaptive rank weighted switching filter for impulsive noise removal in color images. Journal of Real-Time Image Processing, 10(2), 289-311.
[21]. Srinivasan, K. S., & Ebenezer, D. (2007). A new fast and efficient decision-based algorithm for removal of highdensity impulse noises. IEEE Signal Processing Letters, 14(3), 189-192.
[22]. Wang, H., & Deng, Y. (2007, August). Spatial clustering method based on cloud model. In Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on (Vol. 2, pp. 272-276). IEEE.
[23]. Zhou, Z. (2012). Cognition and removal of impulse noise with uncertainty. IEEE Transactions on Image Processing, 21(7), 3157-3167.
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