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


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