Improving the quality of images using Wasserstein generative adversarial networks for image restoration

Abstract

In the present digital age, it is crucial to preserve personal memories and historical photographs in their original form, and this is made possible through image restoration. This paper presents a dynamic multi-scale Wasserstein generative adversarial network with gradient penalty (WGAN GP) framework that combines colorization and image denoising, addressing the limitations of distinct restoration models that denoise and colorize images in parallel. The proposed system adapts to hierarchical image features, stabilizes training, and enhances fine-grained texture reconstruction. The model is trained on CelebA, Places365, and ImageNet datasets. The need for repeated retraining is required, and there are still no guarantees of robustness under various degradations such as fading, saturation loss, and sensor noise. The results show peak signal-to-noise ratio (PSNR) of 24.5 dB and structural similarity index measure (SSIM) of 0.74, outperforming Pix2Pix, CycleGAN, denoising generative adversarial network (D‑GAN), and enhanced super‑resolution generative adversarial network (ESRGAN) in efficiency and robustness. In contrast to previous GAN-based restoration methods that treat denoising and colorization as separate problems, the presented multi-scale WGAN-GP applied a generator-discriminator model, resulting in less training redundancy and similar SSIM results while using ~55-65% less number of training epochs than ESRGAN and DeblurGAN. In the future, the model will integrate attention and transformer-based refinement to enhance detail recovery and perceptual realism further.