Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training, while more complex cases could happen in the real world. This gap between the assumed and actual degradation hurts the restoration performance where artifacts are often observed in the output. However, it is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. To tackle this robustness issue, we propose Diffusion-based Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image. By leveraging a well-performing denoising diffusion probabilistic model, our DR2 diffuses input images to a noisy status where various types of degradation give way to Gaussian noise, and then captures semantic information through iterative denoising steps. As a result, DR2 is robust against common degradation (e.g. blur, resize, noise and compression) and compatible with different designs of enhancement modules. Experiments in various settings show that our framework outperforms state-of-the-art methods on heavily degraded synthetic and real-world datasets.
翻译:失明面部恢复通常会合成退化的低质量数据,并预先确定培训的退化模型,而更复杂的案例则可能在现实世界中发生。假设和实际降解之间的这一差距会损害产出中经常观察到文物的恢复性能。然而,将每一种降解都包括到培训数据中真实世界的案例中来覆盖真实世界案例是昂贵和不可行的。为了解决这种稳健性问题,我们提议Diflif-broust 退化去除器(DR2)首先将退化的图像转换成粗糙但降解性变化的预测,然后使用强化模块将粗糙的预测恢复到高质量的图像。通过利用一种表现良好的去除性扩散概率模型,我们的DR2将输入图像传播到一种噪音状态是昂贵的,而各种类型的降解让高斯噪音进入真实世界案例,然后通过迭代分解步骤获取语义信息。结果,DR2在防止常见的退化(例如模糊、大小、噪声和压缩)和与不同的强化模块设计相容。在各种环境中进行的实验表明我们的框架将退化的合成方法置于高度退化的合成状态上。</s>