Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild. The existing codebook prior mitigates the ill-posedness by leveraging an autoencoder and learned codebook of high-quality (HQ) features, achieving remarkable quality. However, existing approaches in this paradigm frequently depend on a single encoder pre-trained on HQ data for restoring HQ images, disregarding the domain gap between LQ and HQ images. As a result, the encoding of LQ inputs may be insufficient, resulting in suboptimal performance. To tackle this problem, we propose a novel dual-branch framework named DAEFR. Our method introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs. Additionally, we incorporate association training to promote effective synergy between the two branches, enhancing code prediction and output quality. We evaluate the effectiveness of DAEFR on both synthetic and real-world datasets, demonstrating its superior performance in restoring facial details.

翻译：从低质量图像中恢复面部细节一直是一个具有挑战性的问题，这是由于现实环境中各种退化因素导致其具有的病态性。现有基于码本先验的方法通过利用自编码器和高质量特征学习码本来缓解病态性，取得了显著的效果。然而，该范式下的现有方法通常依赖于在高质量数据上预训练的单一编码器来恢复高质量图像，忽略了低质量与高质量图像之间的域差异。这导致对低质量输入的编码可能不够充分，从而产生次优性能。为解决这一问题，我们提出了一种名为DAEFR的新型双分支框架。该方法引入了一个辅助的低质量分支，用于提取低质量输入中的关键信息。此外，我们加入了关联训练以促进两个分支之间的有效协同，从而提升码预测和输出质量。我们在合成和真实数据集上评估了DAEFR的有效性，证明了其在恢复面部细节方面的优越性能。