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International Journal of Sensors, Wireless Communications and Control

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ISSN (Online): 2210-3287

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Research Article

Framework for Image Denoising Employing Different Thresholding Techniques

Author(s): Monika Bharti, Shruti Jain* and Himanshu Jindal*

Volume 14, Issue 3, 2024

Published on: 03 April, 2024

Page: [185 - 203] Pages: 19

DOI: 10.2174/0122103279288431240315044900

Price: $65

Abstract

Background: Noise represents a lack of data in the image which can be removed using Image denoising. Image denoising can be achieved by Gaussian filtering, anisotropic filtering, wavelet Thresholding, etc.

Objective: In this paper, authors have used Wavelet-based denoising because it can effectively remove both additive and multiplicative noise from images, and preserve fine details and edges in the image.

Methods: The different thresholding techniques like Visu Shrink and Bayes Shrink for Hard Thresholding (HT) and Soft Thresholding (ST) employing different standard deviations ranging from 0.05-0.3 with a difference of 0.05 is used.

Results: The peak signal-to-noise ratio (PSNR) is evaluated as a performance parameter. For grayscale images, the maximum value of PSNR is obtained as 29.483 dB while for RGB images, 34.324dB using Bayes Shrink considering ST at 0.05 variance is achieved. 2.2% improvement is observed for grayscale images while 8.6% improvement is observed for RGB images considering Bayes Shrink ST over Bayes Shrink HT.

Conclusion: While comparing PSNR values of other Thresholding techniques, ST results better over HT. The PSNR values for images produced by Bayes Shrink are high which therefore states that the quality of reconstructed images is better for Bayes Shrink than Visu Shrink.

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[1]
Prashar N, Sood M, Jain S. Morphology analysis and time interval measurements using mallat tree decomposition for CVD detection. In: Luhach A, Singh D, Hsiung PA, Hawari K, Lingras P, Singh P, Eds. Advanced Informatics for Computing Research ICAICR 2018 Communications in Computer and Information Science. Singapore: Springer 2019; 955: pp. 171-81.
[http://dx.doi.org/10.1007/978-981-13-3140-4_16]
[2]
Prashar N, Sood M, Jain S. Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal. Trans Inst Meas Contr 2020; 42(4): 854-69.
[http://dx.doi.org/10.1177/0142331219895708]
[3]
Shan GAI, Peng LIU, Jiafeng LIU, Xianglong TANG. A new image denoising algorithm via bivariate shrinkage based on quaternion wavelet transform. J Comput Inf Syst 2010; 6(11): 3751-60.
[4]
Jin J, Yang B, Liang K, Wang X. General image denoising framework based on compressive sensing theory. Comput Graph 2014; 38: 382-91.
[http://dx.doi.org/10.1016/j.cag.2013.11.011]
[5]
Kim JH, Akram F, Choi KN. Image denoising feedback framework using split Bregman approach. Expert Syst Appl 2017; 87: 252-66.
[http://dx.doi.org/10.1016/j.eswa.2017.06.015]
[6]
Laparra V, Gutierrez J, Camps-Valls G, Malo J. Image denoising with kernels based on natural image relations. J Mach Learn Res 2010; 11: 873-903.
[7]
Liu J, Liu R, Wang Y, Chen J, Yang Y, Ma D. Image denoising searching similar blocks along edge directions. Signal Process Image Commun 2017; 57: 33-45.
[http://dx.doi.org/10.1016/j.image.2017.05.001]
[8]
Prashar N, Sood M, Jain S. Design and implementation of a robust noise removal system in ECG signals using dual-tree complex wavelet transform. Biomed Signal Process Control 2021; 63: 102212.
[http://dx.doi.org/10.1016/j.bspc.2020.102212]
[9]
Prashar N, Dogra J, Sood M, Jain S. Removal of electromyography noise from ECG for high performance biomedical systems. New Biol 2018; 8(1): 12-24.
[10]
Biswas M, Om H. A new adaptive image denoising method based on neighboring coefficients. J Inst Eng 2016; 97: 11-9.
[http://dx.doi.org/10.1007/s40031-014-0166-0]
[11]
Salau AO, Jain S. Feature extraction: A survey of the types, techniques and applications. 5th International Conference on Signal Processing and Communication (ICSC-2019). 07-09 March 2019, NOIDA, India. 158-64.
[http://dx.doi.org/10.1109/ICSC45622.2019.8938371]
[12]
Bhusri S, Jain S, Virmani J. Classification of breast lesions using the difference of statistical features. Res J Pharm Biol Chem Sci 2016; 7(4): 1365-72.
[13]
Jain S. Classification of protein kinase B using discrete wavelet transform. Int J Inf Technol 2018; 10(2): 211-6.
[http://dx.doi.org/10.1007/s41870-018-0090-7]
[14]
Jamwal A. Evaluation of correntropy features for normal/glaucoma images employing ridgelet empirical wavelet transform. Maiden edition of IEEE Delhi Section International Conference on Electrical, Electronics and Computer Engineering (DELCON 2022). 11-13 February 2022, New Delhi, India. 1-4.
[15]
Jamwal A, Jain S. Robust multimodal fusion network employing novel Empirical Riglit Wavelet Transform for brain images. Measurement. Sensors 2022; 24: 100529.
[http://dx.doi.org/10.1016/j.measen.2022.100529]
[16]
Priya BS, Basavaraj N. Jagdale, Mukund N Naragud, Vijayalaxmi Hegde. An efficient image denoising based on weiner filter and neigh sure shrink. IJITEE 2019; 9(2): 2278-3075.
[http://dx.doi.org/10.35940/ijitee.a4905.129219]
[17]
Xiaoo Fei, Zhanga Yungang. A comparative study on thresholding methods, in wavelet based image de-noising. Procedia Eng 2011; 15: 3998-4003.
[http://dx.doi.org/10.1016/j.proeng.2011.08.749]
[18]
Shreeyamsha Kumar BK. Image denoising based on gaussian/bilateral filter and its method noise thresholding. SIViP 2012; 7: 1159-72.
[http://dx.doi.org/10.1007/s11760-012-0372-7]
[19]
Mupparaju S, Jahnavi BNVSD. Comparison of various thresholding techniques of image de-noising. Int J Eng Res Technol 2013; 2(9): 3294-301.
[20]
Khan S. De-noising of images based on different wavelet method thresholding by using various shrinkage methods using basic noise conditions. Int J Eng Res Technol 2013; 2(1)
[http://dx.doi.org/10.17577/IJERTV2IS1149]
[21]
Neelima M, Md. Mahabob Pasha. Wavelet transform based on image de-noising using threshlding techniques. Int J Adv Res Comput Commun Eng 2014; 3(9): 7906-8.
[22]
Alla abdela, Ismail uysall. A statistical compartive study on image reconstruction and clustering. IEEE Trans Image Process 2020; 15(2): 430-44.
[23]
Gupta K, Bhowmick B. Coupled Autoencoder Based Reconstruction of Images from Compressively Sampled Measurements 2018 26th European Signal Processing Conference (EUSIPCO). 03-07 September 2018, Rome, Italy. 1067-71.
[http://dx.doi.org/10.23919/EUSIPCO.2018.8553263]
[24]
Wu Xin-Jie, Xu Ming-Da, Li Chang-Di, Ju Chong. Research on image reconstruction algorithms based on autoencoder neural networks of Restricted Boltzmann Machine (RBM) In: Flow Measurement and Instrumentation. Elsevier 2021; 80: pp. 43-56.
[25]
Ehsaeyan E. A novel neighshrink correction algorithm in image-denoising. Iran J Electr Electron Eng 2017; 13(3): 246-56.
[26]
Jain P, Tyagi V. An adaptive edge-preserving image denoising technique using tetrolet transforms. Vis Comput 2015; 31(5): 657-74.
[http://dx.doi.org/10.1007/s00371-014-0993-7]
[27]
Roy S, Sinha N, Sen AK. A new hybrid image denoising method. Int J Inform Technol Knowl Manag 2010; 2(2): 491-7.
[28]
Shi W, Li J, Wu M. An image denoising method based on multiscale wavelet thresholding and bilateral filtering. Wuhan Univ J Nat Sci 2010; 15(2): 148-52.
[http://dx.doi.org/10.1007/s11859-010-0212-y]
[29]
He N, Wang JB, Zhang LL, Lu K. An improved fractional-order differentiation model for image denoising. Signal Processing 2015; 112: 180-8.
[http://dx.doi.org/10.1016/j.sigpro.2014.08.025]
[30]
Prashar N, Sood M, Jain S. Semiautomatic detection of cardiac diseases employing dual tree complex wavelet transform. Period Eng Nat Sci 2018; 6(2): 129-40.
[http://dx.doi.org/10.21533/pen.v6i2.188]
[31]
Bharti M, Saxena S, Kumar R. A middleware approach for reliable resource selection on Internet‐of‐Things. Int J Commun Syst 2020; 33(5): e4278.
[http://dx.doi.org/10.1002/dac.4278]
[32]
Jindal H, Singh H, Bharti M. Modified cuckoo search for resource allocation on social internet-of-things. 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). 20-22 December 2018 ; Solan, India. 465-70.
[http://dx.doi.org/10.1109/PDGC.2018.8745772]
[33]
Bharti M, Jindal H. Modified genetic algorithm for resource selection on internet of things. In: Singh P, Sood S, Kumar Y, Paprzycki M, Pljonkin A, Hong WC, Eds. Futuristic Trends in Networks and Computing Technologies. Springer, Singapore.: Communications in Computer and Information Science 2020; 1206: pp. 164-76.
[http://dx.doi.org/10.1007/978-981-15-4451-4_14]
[34]
Bharti M, Jindal H. Automatic rumour detection model on social media. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). 06-08 November 2020; Waknaghat, India. 367-71.
[http://dx.doi.org/10.1109/PDGC50313.2020.9315738]
[35]
Manreet K, Monika B. Fog computing providing data security: A review. IJCSSE 2014; 4(6): 832-4.
[36]
Mander K, Jindal H. An improved image compression-decompression technique using block truncation and wavelets. Int J Image Graph Signal Process 2017; 9(8): 17.
[37]
Kaur S, Jindal H. Enhanced image watermarking technique using wavelets and interpolation. Int J Image Graph Signal Process 2017; 11(7): 23.
[http://dx.doi.org/10.5815/ijigsp.2017.07.03]
[38]
Jindal H, Bharti M, Kasana SS, Saxena S. An ensemble mosaicing and ridgelet based fusion technique for underwater panoramic image reconstruction and its refinement. Multimedia Tools Appl 2023; 82(22): 33719-71.
[http://dx.doi.org/10.1007/s11042-023-14594-9]
[39]
Aggarwal S, Pandey K. An analysis of PCOS disease prediction model using machine learning classification algorithms. Recent Pat Eng 2021; 15(6): e201021189483.
[http://dx.doi.org/10.2174/1872212115999201224130204]
[40]
Tipirneni L, Patan R. A study on multi-class classification of breast cancer images using ensemble network and transfer learning. Recent Pat Eng 2021; 15(6): e201021187748.
[http://dx.doi.org/10.2174/1872212114999201109205421]
[41]
Zhang H, Ma P, Chen J, Hu Y, Shou H. Nonimaging optical design with supporting quadric method and deep neural network. Recent Pat Eng 2022; 16(6): e071021197064.
[http://dx.doi.org/10.2174/1872212115666211007102718]
[42]
Ghabeli L. Asymmetric successive compute-and-forward and the capacity gap for the gaussian two-way relay channel. Int J Sensors Wirel Commun Control 2023; 13(3): 179-91.
[http://dx.doi.org/10.2174/2210327913666230605120441]
[43]
Bouzegag Y, Djamal T, Abdelmadjid M. Comprehensive performance analysis of soft data fusion schemes under SSDF attacks in cognitive radio networks. Int J Sensors Wirel Commun Control 2022; 12(4): 312-8.
[http://dx.doi.org/10.2174/2210327912666220325155048]
[44]
Kausar R, Gupta A. Optimization of spectral efficiency in massive mimo network for different deployment scenarios. Int J Sensors Wirel Commun Control 2021; 11(6): 700-14.
[http://dx.doi.org/10.2174/2210327910999200821134547]
[45]
Kaur G, Bharti EM. Securing multimedia on hybrid architecture with extended role-based access control. J Bioinform Intell Cont 2014; 3(3): 229-33.
[http://dx.doi.org/10.1166/jbic.2014.1085]
[46]
Bharti M, Kumar R, Saxena S. Architectural survey on internet-of-things. 2019 Fifth International Conference on Image Information Processing (ICIIP). 15-17 November 2019; Shimla, India. 437-42.
[http://dx.doi.org/10.1109/ICIIP47207.2019.8985897]
[47]
Bharti M, Kumar R, Saxena S, Sharma V. A resource-blockchain framework for safeguarding IoT. In: You I, Kim H, Youn TY, Palmieri F, Kotenko I, Eds. MobiSec. Springer, Singapore: Communications in Computer and Information Science 2021; 1544: pp. 107-21.
[http://dx.doi.org/10.1007/978-981-16-9576-6_9]

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