Innovative Image Enhancement via GANs: Addressing Noise, Resolution, and Artifact Challenges

Generative adversarial networks (GANs) are increasingly used for image enhancement tasks like denoising, super-resolution, and artifact removal. In this study, we propose a robust GAN-based architecture featuring a U-Net-style generator and DCNN-based discriminator, optimized for real-time enhanceme...

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Hlavní autoři:	Abdul Wahab Paracha, Syed Fasih Ali Kazmi, Muhammad Abbas, Haris Anjum, Raja Hashim Ali
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-08-01
Edice:	Engineering Proceedings
Témata:	generative adversarial networks (GANs) multi-task image enhancement U-Net generator architecture low-light and noisy image restoration Fréchet inception distance (FID) evaluation
On-line přístup:	https://www.mdpi.com/2673-4591/87/1/104
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