Conditional GANs for Joint Facial Denoising and Multi-Angle Synthesis

This paper presents a two-module conditional GAN pipeline. It first denoises a facial image, then synthesizes the same face at four additional viewing angles. A noise-conditional GAN handles the first stage, taking an explicit noise-level map as input so it can adapt its denoising strength across severities rather than applying one fixed strategy. The second stage is an attention-gated GAN conditioned on the target angle, generating four non-frontal views from a single frontal face while preserving expression. A face-alignment bridge connects the two modules, and experiments evaluate the full pipeline end-to-end across CelebA, KDEF, and real-world images spanning increasing domain distance. Module 1 reaches 38.41 dB PSNR and 0.9776 SSIM at the lowest tested noise level, dropping to 28.91 dB and 0.8575 SSIM at the highest. Module 2 reaches 23.90 dB PSNR and 0.7502 SSIM. The paper also analyzes how the pipeline behaves across domains and reports this in detail.