Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm

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Joseph Isabona 1 Agbotiname Lucky Imoize 2,* Stephen Ojo 3

1. Department of Physics, Federal University Lokoja, Lokoja 260101, Nigeria

2. Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria

3. Department of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC 29621, USA

* Corresponding author.


Received: 2 Apr. 2023 / Revised: 2 Jun. 2023 / Accepted: 30 Jun. 2023 / Published: 8 Oct. 2023

Index Terms

Wavelet transforms, Noisy image, Denoising, BRISQUE, Wavelet thresholding, Improved universal thresholding, Intelligent signal processing


Denoising is a vital aspect of image preprocessing, often explored to eliminate noise in an image to restore its proper characteristic formation and clarity. Unfortunately, noise often degrades the quality of valuable images, making them meaningless for practical applications. Several methods have been deployed to address this problem, but the quality of the recovered images still requires enhancement for efficient applications in practice. In this paper, a wavelet-based universal thresholding technique that possesses the capacity to optimally denoise highly degraded noisy images with both uniform and non-uniform variations in illumination and contrast is proposed. The proposed method, herein referred to as the modified wavelet-based universal thresholding (MWUT), compared to three state-of-the-art denoising techniques, was employed to denoise five noisy images. In order to appraise the qualities of the images obtained, seven performance indicators comprising the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Structural Content (SC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Signal-to-Reconstruction-Error Ratio (SRER), Blind Spatial Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) were employed. The first five indicators – RMSE, MAE, SC, PSNR, SSIM, and SRER- are reference indicators, while the remaining two – NIQE and BRISQUE- are referenceless. For the superior performance of the proposed wavelet threshold algorithm, the SC, PSNR, SSIM, and SRER must be higher, while lower values of NIQE, BRISQUE, RMSE, and MAE are preferred. A higher and better value of PSNR, SSIM, and SRER in the final results shows the superior performance of our proposed MWUT denoising technique over the preliminaries. Lower NIQE, BRISQUE, RMSE, and MAE values also indicate higher and better image quality results using the proposed modified wavelet-based universal thresholding technique over the existing schemes. The modified wavelet-based universal thresholding technique would find practical applications in digital image processing and enhancement.

Cite This Paper

Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, "Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.5, pp. 1-16, 2023. DOI:10.5815/ijigsp.2023.05.01


[1]X. Zhu, P. Milanfar, Automatic parameter selection for denoising algorithms using a no-reference measure of image content, IEEE Trans. Image Process. 19 (2010) 3116–3132.
[2]Y. Qian, Image denoising algorithm based on improved wavelet threshold function and median filter, in: 2018 IEEE 18th Int. Conf. Commun. Technol., IEEE, 2018: pp. 1197–1202.
[3]G. Deng, Z. Liu, A wavelet image denoising based on the new threshold function, in: 2015 11th Int. Conf. Comput. Intell. Secur., IEEE, 2015: pp. 158–161.
[4]Z. Jianhua, Z. Qiang, Z. Jinrong, S. Lin, W. Jilong, A novel algorithm for threshold image denoising based on wavelet construction, Cluster Comput. 22 (2019) 12443–12450.
[5]M.A. Adelabu, A.L. Imoize, K.E. Adesoji, Enhancement of a Camera-Based Continuous Heart Rate Measurement Algorithm, SN Comput. Sci. 3 (2022) 284.
[6]T. Dixit, N. Singh, Proposing a Framework to Analyze Breast Cancer in Mammogram Images Using Global Thresholding, Gray Level Co‚ÄźOccurrence Matrix, and Convolutional Neural Network (CNN), Adv. Data Sci. Anal. Concepts Paradig. (2023) 145–180.
[7]J. Isabona, Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction, Wirel. Pers. Commun. 114 (2020) 3635–3653.
[8]J. Isabona, R. Kehinde, Multi-resolution based discrete wavelet transform for enhanced signal coverage processing and prediction analysis, FUDMA J. Sci. 3 (2019) 6–15.
[9]C. Ebhota, J. Isabona, V.M. Srivastava, Improved adaptive signal power loss prediction using combined vector statistics based smoothing and neural network approach, Prog. Electromagn. Res. C. 82 (2018) 155–169.
[10]P.-L. Shui, Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain, IEEE Signal Process. Lett. 12 (2005) 681–684.
[11]A. Foi, V. Katkovnik, K. Egiazarian, Signal-dependent noise removal in pointwise shape-adaptive DCT domain with locally adaptive variance, in: 2007 15th Eur. Signal Process. Conf., IEEE, 2007: pp. 2159–2163.
[12]S. Solbo, T. Eltoft, Homomorphic wavelet-based statistical despeckling of SAR images, IEEE Trans. Geosci. Remote Sens. 42 (2004) 711–721.
[13]H.-L. Eng, K.-K. Ma, Noise adaptive soft-switching median filter for image denoising, in: 2000 IEEE Int. Conf. Acoust. Speech, Signal Process. Proc. (Cat. No. 00CH37100), IEEE, 2000: pp. 2175–2178.
[14]H.-L. Eng, K.-K. Ma, Noise adaptive soft-switching median filter, IEEE Trans. Image Process. 10 (2001) 242–251.
[15]K.E. Barner, G.R. Arce, Nonlinear signal and image processing: theory, methods, and applications, CRC Press, 2003.
[16]L. Fan, F. Zhang, H. Fan, C. Zhang, Brief review of image denoising techniques, Vis. Comput. Ind. Biomed. Art. 2 (2019) 1–12.
[17]R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing Using Matlab, 2004.
[18]X. Liu, M. Tanaka, M. Okutomi, Single-image noise level estimation for blind denoising, IEEE Trans. Image Process. 22 (2013) 5226–5237.
[19]X. Liu, M. Tanaka, M. Okutomi, Signal dependent noise removal from a single image, in: 2014 IEEE Int. Conf. Image Process., IEEE, 2014: pp. 2679–2683.
[20]M.A. Adelabu, A.L. Imoize, G.U. Ughegbe, Performance Evaluation of Radio Frequency Interference Measurements from Microwave Links in Dense Urban Cities, Telecom. 2 (2021) 328–368.
[21]G.U. Ughegbe, M.A. Adelabu, A.L. Imoize, Experimental data on radio frequency interference in microwave links using frequency scan measurements at 6 GHz, 7 GHz, and 8 GHz, Data Br. 35 (2021) 106916.
[22]E. Srinivasan, D. Ebenezer, New Nonlinear Filtering Strategies for eliminating Medium and Long tailed noise in images with edge preservation properties, IETE J. Educ. 46 (2005) 3–11.
[23]L.J. Halliwell, Classifying the tails of loss distributions, Casualty Actuar. Soc. 2 (2013) 1–27.
[24]F. Ming, H. Long, Partial discharge de-noising based on hybrid particle swarm optimization SWT adaptive threshold, IEEE Access. (2023).
[25]X. Qiao, J. Bao, H. Zhang, L. Zeng, D. Li, Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform, Inf. Process. Agric. 4 (2017) 206–213.
[26]K.V.N. Kavitha, S. Ashok, A.L. Imoize, S. Ojo, K.S. Selvan, T.A. Ahanger, M. Alhassan, On the Use of Wavelet Domain and Machine Learning for the Analysis of Epileptic Seizure Detection from EEG Signals, J. Healthc. Eng. 2022 (2022) 8928021.
[27]D.L. Donoho, J.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika. 81 (1994) 425–455.
[28]D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage, J. Am. Stat. Assoc. 90 (1995) 1200–1224.
[29]A.S. Dixit, P. Sharma, A Comparative Study of Wavelet Thresholding for Image Denoising, Int. J. Image, Graph. Signal Process, vol.6, no.12, pp.39-46, 2014.
[30]X. Wang, X. Ou, B.-W. Chen, M. Kim, Image denoising based on improved wavelet threshold function for wireless camera networks and transmissions, Int. J. Distrib. Sens. Networks. 11 (2015) 670216.
[31]C.F. Cunha, M.R. Petraglia, A.T. Carvalho, A.C.S. Lima, A Wavelet Threshold Function for Treatment of Partial Discharge Measurements, in: Wavelet Theory, IntechOpen, 2020.
[32]B. Xie, Z. Xiong, Z. Wang, L. Zhang, D. Zhang, F. Li, Gamma spectrum denoising method based on improved wavelet threshold, Nucl. Eng. Technol. 52 (2020) 1771–1776.
[33]K.V.N. Kavitha, A. Shanmugam, A.L. Imoize, Optimized deep knowledge-based no-reference image quality index for denoised MRI images, Sci. African. 20 (2023) e01680.
[34]A.S. Akinfende, A.L. Imoize, O.S. Ajose, Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition, Indones. J. Electr. Eng. Comput. Sci. 19 (2020) 1275–1286.
[35]P. Singhai, A. Kumar, A. Ateek, I.A. Ansari, G.K. Singh, H.N. Lee, ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques, Circuits, Syst. Signal Process. (2023) 1–29.
[36]S.O. Ajose, A.L. Imoize, Propagation measurements and modelling at 1800 MHz in Lagos Nigeria, Int. J. Wirel. Mob. Comput. 6 (2013) 165–174.
[37]A.L. Imoize, A.E. Ibhaze, P.O. Nwosu, S.O. Ajose, Determination of Best-fit Propagation Models for Pathloss Prediction of a 4G LTE Network in Suburban and Urban Areas of Lagos, Nigeria, West Indian J. Eng. 41 (2019) 13–21.
[38]A.E. Ibhaze, A.L. Imoize, S.O. Ajose, S.N. John, C.U. Ndujiuba, F.E. Idachaba, An Empirical Propagation Model for Path Loss Prediction at 2100MHz in a Dense Urban Environment, Indian J. Sci. Technol. 10 (2017) 1–9.
[39]D.O. Ojuh, J. Isabona, Optimum signal denoising based on wavelet shrinkage thresholding techniques: white Gaussian noise and white uniform noise case study, J. Sci. Eng. Res. 5 (2018) 179–186.
[40]V.C. Ebhota, J. Isabona, V.M. Srivastava, Environment-Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells, Wirel. Pers. Commun. 104 (2019) 935–948.
[41]I.K. Okakwu, E.S. Oluwasogo, A.E. Ibhaze, A.L. Imoize, A comparative study of time series analysis for forecasting energy demand in Nigeria, Niger. J. Technol. 38 (2019) 465.
[42]F. Memon, M.A. Unar, S. Memon, Image quality assessment for performance evaluation of focus measure operators, Mehran Univ. Res. J. Eng. Technol. 34 (2015) 379–386.
[43]A. Sabir, K. Khurshid, A. Salman, Segmentation-based image defogging using modified dark channel prior, EURASIP J. Image Video Process. 2020 (2020) 1–14.
[44]Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13 (2004) 600–612.
[45]A. Mittal, R. Soundararajan, A.C. Bovik, Making a “completely blind” image quality analyzer, IEEE Signal Process. Lett. 20 (2012) 209–212.
[46]A. Mittal, A.K. Moorthy, A.C. Bovik, No-reference image quality assessment in the spatial domain, IEEE Trans. Image Process. 21 (2012) 4695–4708.
[47]G. Wang, Y. Zhang, W.-F. Xie, Y. Qu, L. Feng, Hyperspectral linear unmixing based on collaborative sparsity and multi-band non-local total variation, Int. J. Remote Sens. 43 (2022) 1–26.
[48]U. Sara, M. Akter, M.S. Uddin, Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study, J. Comput. Commun. 7 (2019) 8–18.
[49]A. Mittal, A.K. Moorthy, A.C. Bovik, Blind/referenceless image spatial quality evaluator, in: 2011 Conf. Rec. Forty Fifth Asilomar Conf. Signals, Syst. Comput., IEEE, 2011: pp. 723–727.
[50]J. Isabona, D.O. Ojuh, Wavelet selection based on wavelet transform for optimum noisy signal processing, Int. J. Basic Appl. Sci. 3 (2017) 57–65.