GAN-based image restoration inverts the generative process to repair images corrupted by known degradations. Existing unsupervised methods must be carefully tuned for each task and degradation level. In this work, we make StyleGAN image restoration robust: a single set of hyperparameters works across a wide range of degradation levels. This makes it possible to handle combinations of several degradations, without the need to retune. Our proposed approach relies on a 3-phase progressive latent space extension and a conservative optimizer, which avoids the need for any additional regularization terms. Extensive experiments demonstrate robustness on inpainting, upsampling, denoising, and deartifacting at varying degradations levels, outperforming other StyleGAN-based inversion techniques. Our approach also favorably compares to diffusion-based restoration by yielding much more realistic inversion results. Code will be released upon publication.

翻译：基于GAN的图像恢复通过逆转生成过程来修复因已知退化而受损的图像。现有无监督方法需针对每个任务和退化程度仔细调参。在本工作中，我们使StyleGAN图像恢复具备鲁棒性：一组超参数即可在广泛退化程度范围内有效工作，使得无需重新调参即可处理多种退化的组合。提出的方法依赖于三阶段渐进式潜在空间扩展和保守优化器，无需任何额外正则项。大量实验表明，该方法在图像修复、上采样、去噪和去伪影等不同退化程度任务中展现出鲁棒性，性能优于其他基于StyleGAN的逆向技术。我们的方法还能产生比扩散模型恢复更逼真的逆向结果，具有优势。代码将在发表后开源。