Blind face deblurring aims to recover a sharper face from its unknown degraded version (i.e., different motion blur, noise). However, most previous works typically rely on degradation facial priors extracted from low-quality inputs, which generally leads to unlifelike deblurring results. In this paper, we propose a multi-scale progressive face-deblurring generative adversarial network (MPFD-GAN) that requires no facial priors to generate more realistic multi-scale deblurring results by one feed-forward process. Specifically, MPFD-GAN mainly includes two core modules: the feature retention module and the texture reconstruction module (TRM). The former can capture non-local similar features by full advantage of the different receptive fields, which facilitates the network to recover the complete structure. The latter adopts a supervisory attention mechanism that fully utilizes the recovered low-scale face to refine incoming features at every scale before propagating them further. Moreover, TRM extracts the high-frequency texture information from the recovered low-scale face by the Laplace operator, which guides subsequent steps to progressively recover faithful face texture details. Experimental results on the CelebA, UTKFace and CelebA-HQ datasets demonstrate the effectiveness of the proposed network, which achieves better accuracy and visual quality against state-of-the-art methods.

盲人脸去模糊旨在从未知的退化版本（即不同的运动模糊、噪声）中恢复更清晰的人脸。然而，大多数先前的工作通常依赖于从低质量输入中提取的退化面部先验，这通常会导致不逼真的去模糊结果。在本文中，我们提出了一种多尺度渐进式人脸去模糊生成对抗网络（MPFD-GAN），它不需要面部先验，通过一个前馈过程生成更真实的多尺度去模糊结果。具体来说，MPFD-GAN 主要包括两个核心模块：特征保留模块和纹理重建模块（TRM）。前者可以充分利用不同的感受野来捕获非局部的相似特征，有利于网络恢复完整的结构。后者采用监督注意机制，充分利用恢复的低尺度人脸来细化每个尺度的输入特征，然后再进一步传播它们。此外，TRM通过拉普拉斯算子从恢复的低尺度人脸中提取高频纹理信息，指导后续步骤逐步恢复忠实的人脸纹理细节。在 CelebA、UTKFace 和 CelebA-HQ 数据集上的实验结果证明了所提出的网络的有效性，与最先进的方法相比，它实现了更好的准确性和视觉质量。它指导后续步骤逐步恢复忠实的面部纹理细节。在 CelebA、UTKFace 和 CelebA-HQ 数据集上的实验结果证明了所提出的网络的有效性，与最先进的方法相比，它实现了更好的准确性和视觉质量。它指导后续步骤逐步恢复忠实的面部纹理细节。在 CelebA、UTKFace 和 CelebA-HQ 数据集上的实验结果证明了所提出的网络的有效性，与最先进的方法相比，它实现了更好的准确性和视觉质量。