Historical Image Restoration Using GFPGAN-Based Face-Centered Enhancement Mechanism to Address Blur and Low-Light Degradation - Ardi Wijaya; Rozali Toyib (Informatika, Universitas Jenderal Soedirman, 2026)
Archaic image restoration faces significant challenges due to complex degradation in the form of blurring and attenuation of extreme luminance (low-light) that obscure the identity of historical subjects. This study constructs a new paradigm through the Face-Centered Enhancement mechanism based on GFPGAN to reconstruct high-fidelity facial features in visual archives from the Bengkulu Museum, Bung Karno's Exile House, and Fort Marlborough. The novelty of this study lies in the integration of a feature enhancement module capable of performing adaptive deconvolution specifically on the face area to mitigate stochastic hallucinations in the GAN latent space, thus balancing lighting restoration without distorting the authenticity of the original character of historical figures. Quantitative evaluation of 50 images using a synthetic degradation scheme shows superior performance, where 95% of the data achieves SSIM ≥ 0.95 and MSE ≤ 0.01. This improvement in visual quality has direct implications for the efficiency of the OCR system in extracting document text and optimizing classification in digital archival information systems. Despite its dependence on high-performance computing, this method has proven effective in bridging the disparity between improving pixel quality and preserving historical integrity in national digital preservation efforts.