A Hybrid Adversarial Learning Method of Stable and High-Fidelity Image Restoration

To restore images, it is necessary to have models which are capable of delivering perceptual quality and stability as well as semantic accuracy. Despite the fact that GANbased solutions have managed to reach significant advances, such as Cycle-GAN, SRGAN, or DCGAN, all of them possess certain flaws. CycleGAN is struggling with diversity and artefact, SRGAN would be most probably used to super-resolution only, and DCGAN cannot contemplate the complex features in semantic. To address these drawbacks, we suggested implementing a hybrid single architecture which makes use of the benefits of various GAN architectures. It applies cycle-consistency to maintain semantic connections, perceptual learning to make textures and fine details deeper and stable adversarial learning to generate realistic images. In addition, the training of both features and latent regularization can stabilize and enable the one-to-many and diverse mappings. When equipped with the synergistic solutions, the suggested GAN model presents a robust foundation of encouraging semantically adherent and pristine quality image restoration.