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 is available at https://lvsn.github.io/RobustUnsupervised/.

翻译：基于GAN的图像恢复通过反转生成过程来修复已知退化损坏的图像。现有无监督方法需要针对每个任务和退化级别仔细调参。本文使StyleGAN图像恢复具有鲁棒性：单组超参数即可在广泛的退化级别范围内有效工作，从而无需重新调参即可处理多种退化组合。我们提出的方法采用三阶段渐进式潜空间扩展和保守优化器，无需任何额外正则化项。大量实验表明，该方法在补全、上采样、去噪和去伪影等不同退化级别任务中具有鲁棒性，优于其他基于StyleGAN的反演技术。相比扩散式恢复方法，我们的方法还能生成更真实的反演结果。代码见https://lvsn.github.io/RobustUnsupervised/。