Image and Signal Denoising Methods
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This paper highlights the potential of wavelet transform as a powerful tool for image analysis and noise removal, and the use of intelligent thresholding techniques enhances the accuracy of the process, making this approach suitable for applications requiring high-quality images.
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Image denoising is a vital process in the field of digital image processing, which aims to improve the quality of images by eliminating unwanted components resulting from various noise sources. This article presents an innovative approach based on wavelet transform and thresholding techniques to effectively remove noise while preserving important details and features in the image. In this article, grayscale images are analyzed using wavelet transform, which allows the image to be divided into multiple frequency components. This technique enables the important information to be distinguished from the noise that is often concentrated in the high-frequency components. After the analysis, carefully designed dynamic thresholds are applied to remove noise from the high-frequency components while preserving the main image signals. Finally, the image is reconstructed from the components that have been modified to be free of noise. The implementation was done using MATLAB to achieve high efficiency and accuracy. The results demonstrate that the method offers an excellent balance between noise removal and image detail preservation. This paper highlights the potential of wavelet transform as a powerful tool for image analysis and noise removal. In addition, the use of intelligent thresholding techniques enhances the accuracy of the process, making this approach suitable for applications requiring high-quality images such as medical imaging, computer vision systems, and satellite image processing. This work provides an advanced path to image quality improvement, making it a promising option for modern applications in digital image processing.
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@article{Ahmed2026Image,
title = {Image Denoising Using Wavelet Transform and Thresholding Techniques: An Advanced Approach to Improving Image Quality},
author = {Israa Mohammed Ahmed},
journal = {Journal of Intelligent Decision Making and Information Science},
year = {2026},
doi = {10.59543/jidmis.v3.1393},
url = {https://doi.org/10.59543/jidmis.v3.1393}
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