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Upscaling images typically depends on facial features, such as facial geometry details or references, to rebuild reasonable details. The low-quality input images cannot offer accurate geometric details, and the high-quality details are obscure, limiting performance in practical conditions. This work addresses the problem using an enhanced version of the generative adversarial network (GAN) model that uses rich and varied facial features incorporated into the pretrained StyleGAN2 for face restoration. The generated facial features are integrated into the facial restoration process using spatial feature transform layers to achieve facial details and color quality to improve reliability. In particular, the image background is upsampled using a modified enhanced super-resolution GAN trained in parallel to remove noise while recreating a high-resolution image from low-quality input. The upscale image resolution GAN is used …