鲁棒的无监督StyleGAN图像复原
Robust Unsupervised StyleGAN Image Restoration
AI总结：
本文提出一种鲁棒的无监督StyleGAN图像复原方法，通过三阶段渐进式潜空间扩展和保守优化器，实现单一超参数适用于多种退化水平及组合，优于现有StyleGAN逆变换和扩散复原方法。
摘要：
基于GAN的图像复原通过逆生成过程来修复受已知退化影响的图像。现有的无监督方法必须针对每个任务和退化水平仔细调参。在这项工作中，我们使StyleGAN图像复原变得鲁棒：一组超参数即可适用于广泛的退化水平范围。这使得处理多种退化的组合成为可能，而无需重新调参。我们提出的方法依赖于三阶段渐进式潜空间扩展和保守优化器，从而无需任何额外的正则化项。大量实验表明，该方法在修复、上采样、去噪和去伪影等不同退化水平下均表现出鲁棒性，优于其他基于StyleGAN的逆变换技术。我们的方法在生成更逼真的逆变换结果方面也优于基于扩散的复原方法。代码可在https://lvsn.github.io/RobustUnsupervised/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
