Advanced Noise Mitigation Strategies in Image Processing: A Comprehensive Analysis and Optimization Study
DOI:
https://doi.org/10.26438/ijcse/v10i12.4750Keywords:
Image capturing, Noise handling mechanism, Filtering, Parameter enhancementAbstract
The advent of technology has shifted the representation of information from text to images. However, the image capturing process introduces noise, resulting in distortion and the generation of potentially misleading information. To address this challenge, it is essential to integrate noise handling mechanisms into existing image processing methods. Among these mechanisms, filtering stands out as a crucial strategy for mitigating noise effects. This research delves into the analysis of various noise handling mechanisms in the current context, aiming to identify optimized strategies for enhancing parameters in future implementations.
References
[1] S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, and M. Buckland, "A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for brain tumor diagnosis," Neural Information Processing, vol.3536, no. c, pp.1–13, 2016.
[2] P. Yuvarani, "Image Denoising and Enhancement for Lung Cancer Detection using Soft Computing Technique," in International Conference on Image Processing, pp.27–30, 2012.
[3] I. H. Witten, A. Moffat, and T. C. Bell, "Managing gigabytes: compressing and indexing documents and images," 1999.
[4] B. V. Kiranmayee, T. V. Rajinikanth, and S. Nagini, "Enhancement of SVM based MRI Brain Image Classification using Pre-Processing Techniques," August, p. 9, 2016.
[5] D. Selvaraj, "MRI BRAIN IMAGE SEGMENTATION TECHNIQUES - A REVIEW," Computer Science and Technology, vol.4, no.5, pp.364–381, 2013.
[6] S. Begum, D. Chakraborty, and R. Sarkar, "Data Classification Using Feature Selection and kNN Machine Learning Approach," in International Conference on Computational Intelligence and Communication Networks, pp.811–814, 2015.
[7] A. Singh, "Analysis of Image Noise Removal Methodologies for High-Density Impulse Noise," vol.3, no. 6, pp.659–665, 2014.
[8] A. Rani, A. K. Bhullar, D. Dangwal, and S. Kumar, "A Zero-Watermarking Scheme using Discrete Wavelet Transform," pp.603–609, 2015.
[9] I. D. T. IDT, B. Goossens, and W. Philips, "MRI Segmentation of the Human Brain: Challenges, Methods, and Applications," 2015.
[10] P. Singh, "A Comparative Study to Noise Models and Image Restoration Techniques," vol.149, no.1, pp.18–27, 2016.
[11] T. K. Djidjou, D. A. Bevans, S. Li, and A. Rogachev, "Observation of Shot Noise in Phosphorescent Organic Light-Emitting Diodes," vol.61, no.9, pp.3252–3257, 2014.
[12] S. H. Teoh and H. Ibrahim, "Median Filtering Frameworks for Reducing Impulse Noise from Grayscale Digital Images: A Literature Survey," vol.1, no.4, pp.4–7, 2012.
[13] C. Khare and K. K. Nagwanshi, "Image Restoration Technique with Non-Linear Filters," pp.1–5, 2011.
[14] S. Shrestha, "Image Denoising Using New Adaptive-Based Median Filter," Signal Image Process., vol.5, no.4, pp.1–13, 2014.
[15] E. A. Kumari, "A Survey on Filtering Technique for Denoising Images in Digital Image Processing," vol.4, no.8, pp.612–614, 2014.
[16] G. Deng and L. W. Cahill, "An Adaptive Gaussian Filter for Noise Reduction and Edge Detection," pp.1615–1619, 1993.
[17] E. E. Kerre, D. Van De Ville, M. Nachtegael, D. Van Der Weken, and E. E. Kerre, "Noise reduction by fuzzy image filtering," IEEE, p. 125050, January 2013.
[18] T. R. Jeyalakshmi and K. Ramar, "A Modified Method for Speckle Noise Removal in Ultrasound Medical Images," vol. 2, no. 1, pp.54–58, 2010.
[19] R. Pandey, A. Awasthi, and V. Srivastava, "Comparison between Bit Error Rate and Signal to Noise Ratio in OFDM Using LSE," pp.463–466, 2013.
[20] K. M. S. Raju, M. S. Nasir, and T. M. Devi, "Filtering Techniques to reduce Speckle Noise and Image Quality Enhancement methods on Satellite Images," vol.15, no.4, pp.10–15, 2013.
[21] A. June, "Fuzzy-Based New Algorithm For Noise Removal And Edge Detection," vol.2, no.2, 2014.
[22] P. S. J. Sree, P. Kumar, R. Siddavatam, and R. Verma, "Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets," vol.7, no.1, pp.111–118, 2011.
[23] F. Khalvati, "Computational Redundancy in Image Processing," Image (Rochester, N.Y.), November 2008.
[24] X. Zhang, X. Li, Z. Tang, S. Zhang, and S. Xie, "Noise removal in embedded image with bit approximation," IEEE Transactions on Knowledge and Data Engineering, vol.34, no.3, pp.1359–1369, 2020.
[25] A. H. Pilevar, S. Saien, M. Khandel, and B. Mansoori, "A new filter to remove salt and pepper noise in color images," Signal, Image Video Process., vol.9, no.4, pp.779–786, 2015.
[26] M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process., vol.15, no.12, pp.3736–3745, 2006.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
