Restoration of Adversarial Network Through Image Processing and Machine Learning Algorithms

Abstract

Image restoration and enhancement are essential in several digital imaging applications, including medical diagnostics, remote sensing, and the preservation of historical documents. Conventional restoration methods, while successful in some contexts, often fail to address intricate textures and significantly deteriorated photos. This research presents a sophisticated picture restoration and clarity improvement technique using Generative Adversarial Networks (GANs), which integrates a style perception module and a multi-scale attention mechanism (SP-MSA-IR). The model integrates the detailed feature extraction abilities of GANs with the comprehensive contextual comprehension provided by attention modules to enhance visual fidelity and structural coherence. A dual-channel restoration network using auxiliary identity images (DC-IRN) is presented to improve restoration accuracy and identification consistency. Experimental assessments on the Helen Face and CelebA datasets demonstrate that the suggested approach regularly surpasses current methodologies, attaining greater PSNR (up to 52.84 dB), SSIM (up to 0.968), and reduced RMSE. The findings illustrate the model's capacity to generate high-quality, semantically consistent pictures while ensuring efficient runtime and resource utilisation.