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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ahmed A. Mostfa
Ninevah University
Computer Science & IT Engineering

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Improving image processing and noise removal using generative deep learning techniques Firas Abdulrahman Yousif; Shamil Kh. Ramadhan; Ahmed A. Mostfa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4667-4678

Abstract

Denoising plays a crucial role in satellite and medical images. The classical methods struggle to find out the noisy values and might lead to some loss in image metadata’s essitals. In this study, a generative adversarial network (GAN) is introduced along with the U-Net architecture. GAN algorithm has the capability to provide a powerful estimated fine image compared with a regular neural network approach. The Modified National Institute of Standards and Technology (MNIST) dataset is injected with synthetic random noise. The proposed work demonstrated noise reduction capabilities and significant improvement in peak signal to noise ratio (PSNR), from 11.70 dB for noise image to 19.41 dB for the estimated image. The structural similarity index (SSIM) moved from 0.61 to 0.93, showing a big restoration with stable convergence for the image details. The GAN visual analysis based on deep learning showed effective output detail recovery.
Co-Authors Firas Abdulrahman Yousif Shamil Kh. Ramadhan