Title: A unified framework for image denoising using enhanced super-resolution generative adversarial networks

Authors: Arti Jain; Anand Singh Jalal; Chetan Singh Negi

Addresses: Department of Computer Science and Engineering, National Institute of Technology Raipur, Chhatisgarh, India ' School of Computer Science and Information Technology, Devi Ahilya Vishwavidyalaya, Indore (M.P.), India ' Department of Computer Science and Engineering, GBPUAT University, Pantnagar, Uttrakhand, India

Abstract: Image denoising is a task to remove corrupted pixels from an image. The corrupted pixel betides to the image while capturing and transmitting from various sources. For image denoising, many filtering approaches are used in the literature. Most of the methods are successfully capable to suppress the noisy pixels. However, the resolution of the image deteriorated during the denoising process. Therefore, in the proposed work denoising is unified with the super-resolution technique to get a noise-free informative and magnificent image. In the proposed approach first denoising is performed with the weighted median filter. The weighted median filter has been extensively used and preferred as it minimises loss of information by filtering out background noise without generating unrealistic pixels. The denoised image is then passed to a super-resolution technique to boost the perceiving quality of the denoised image. In light of PSNR and SSIM, it has been evident that the proposed method effectively outcompetes the existing state-of-the-art denoising methods.

Keywords: image denoising; super-resolution; deep neural networks; generative adversarial network; GAN.

DOI: 10.1504/IJIEI.2025.148581

International Journal of Intelligent Engineering Informatics, 2025 Vol.13 No.3, pp.297 - 319

Received: 27 Feb 2024
Accepted: 01 Aug 2024
Published online: 14 Sep 2025 *

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