一种用于超分辨率重建多种低分辨率人脸图像的统一框架
A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions
AI总结：
针对现有FSR需按不同低分辨率分别训练模型的问题，提出由三个锚点自编码器、特征权重回归器和解码器构成的统一框架，一次训练即可在多种低分辨率下取得鲁棒的先进性能。
摘要：
现有的人脸图像超分辨率（FSR）算法通常针对特定的低输入分辨率训练一个特定模型，以获得最佳结果。相比之下，本文探索了一种统一框架，只需训练一次，随后即可用于超分辨率重建多种低分辨率的输入人脸图像。为此，我们提出了一种新颖的神经网络架构，由三个锚点自编码器、一个特征权重回归器和一个最终图像解码器组成。三个锚点自编码器分别用于针对三个预定义低输入分辨率（或称锚点分辨率）实现最优FSR。任意低分辨率的输入人脸图像首先通过双三次插值上采样到目标分辨率，然后并行输入三个自编码器。随后，三个编码后的锚点特征以特征权重回归器确定的权重进行融合。最后，融合特征被送入最终图像解码器，以得到超分辨率结果。实验表明，所提算法通过单一框架在广泛的低输入分辨率范围内均取得了鲁棒且最先进的性能。代码和模型将在本工作发表后公开。
英文摘要：
The existing face image super-resolution (FSR) algorithms usually train a specific model for a specific low input resolution for optimal results. By contrast, we explore in this work a unified framework that is trained once and then used to super-resolve input face images of varied low resolutions. For that purpose, we propose a novel neural network architecture that is composed of three anchor auto-encoders, one feature weight regressor and a final image decoder. The three anchor auto-encoders are meant for optimal FSR for three pre-defined low input resolutions, or named anchor resolutions, respectively. An input face image of an arbitrary low resolution is firstly up-scaled to the target resolution by bi-cubic interpolation and then fed to the three auto-encoders in parallel. The three encoded anchor features are then fused with weights determined by the feature weight regressor. At last, the fused feature is sent to the final image decoder to derive the super-resolution result. As shown by experiments, the proposed algorithm achieves robust and state-of-the-art performance over a wide range of low input resolutions by a single framework. Code and models will be made available after the publication of this work.
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。
