论文标题
盲面修复：基准数据集和基线模型
Blind Face Restoration: Benchmark Datasets and a Baseline Model
论文作者
论文摘要
盲面修复（BFR）旨在从相应的低质量（LQ）输入中构建高质量（HQ）面部图像。最近，已经提出了许多BFR方法，并取得了巨大的成功。但是，这些方法是在私人合成的数据集上进行培训或评估的，这使得它与随后与它们进行公平比较的方法不可行。为了解决这个问题，我们首先合成了两个盲面修复基准数据集，称为Edface-Celeb-1m（BFR128）和Edface-Celeb-150k（BFR512）。在五个设置下，将最先进的方法在它们的五个设置下进行了基准测试，包括模糊，噪声，低分辨率，JPEG压缩伪像及其组合（完全退化）。为了使比较更全面，应用了五个广泛使用的定量指标和两个任务驱动的指标，包括平均面部标志距离（AFLD）和平均面部ID余弦相似性（AFICS）。此外，我们开发了一个有效的基线模型，称为Swin Transformer U-NET（昏迷）。带有U-NET体系结构的昏迷器采用了注意机制和移动的窗口方案，以捕获长距离的像素相互作用，并更多地关注重要特征，同时仍受到有效的训练。实验结果表明，所提出的基线方法对各种BFR任务的SOTA方法表现出色。
Blind Face Restoration (BFR) aims to construct a high-quality (HQ) face image from its corresponding low-quality (LQ) input. Recently, many BFR methods have been proposed and they have achieved remarkable success. However, these methods are trained or evaluated on privately synthesized datasets, which makes it infeasible for the subsequent approaches to fairly compare with them. To address this problem, we first synthesize two blind face restoration benchmark datasets called EDFace-Celeb-1M (BFR128) and EDFace-Celeb-150K (BFR512). State-of-the-art methods are benchmarked on them under five settings including blur, noise, low resolution, JPEG compression artifacts, and the combination of them (full degradation). To make the comparison more comprehensive, five widely-used quantitative metrics and two task-driven metrics including Average Face Landmark Distance (AFLD) and Average Face ID Cosine Similarity (AFICS) are applied. Furthermore, we develop an effective baseline model called Swin Transformer U-Net (STUNet). The STUNet with U-net architecture applies an attention mechanism and a shifted windowing scheme to capture long-range pixel interactions and focus more on significant features while still being trained efficiently. Experimental results show that the proposed baseline method performs favourably against the SOTA methods on various BFR tasks.
