In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the low-resolution facial images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from most competing super-resolution techniques that rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of 2× . This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. The proposed C-SRIP model (Cascaded Super Resolution with Identity Priors) is able to upscale (tiny) low-resolution images captured in unconstrained conditions and produce visually convincing results for diverse low-resolution inputs. We rigorously evaluate the proposed model on the Labeled Faces in the Wild (LFW), Helen and CelebA datasets and report superior performance compared to the existing state-of-the-art.

中文翻译：

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使用级联超分辨率和身份优先级进行面部幻觉。

在本文中，我们解决了在高放大倍数下从低分辨率输入产生高分辨率面部图像的幻觉的问题。我们用卷积神经网络（CNN）来完成这项任务，并提出了一种新颖的（深层）面部幻觉模型，该模型将身份先验纳入学习过程。该模型由两个主要部分组成：i）放大低分辨率面部图像的级联超分辨率网络，以及ii）在训练过程中充当超分辨率网络身份先验的面部识别模型的集合。与大多数竞争性超分辨率技术依赖于单个模型进行放大（甚至具有较大的放大倍数）不同，我们的网络使用多个SR模型的级联，以2x的步长逐步放大低分辨率图像。此特征使我们能够以不同的分辨率应用监视信号（目标外观），并在多个尺度上合并身份约束。所提出的C-SRIP模型（具有先验身份的级联超分辨率）能够放大（微小）在不受限制的条件下捕获的低分辨率图像，并为各种低分辨率输入产生视觉上令人信服的结果。我们在野外标记的脸谱（LFW），Helen和CelebA数据集上严格评估了建议的模型，并报告了与现有最​​新技术相比更出色的性能。所提出的C-SRIP模型（具有先验身份的级联超分辨率）能够放大（微小）在不受限制的条件下捕获的低分辨率图像，并为各种低分辨率输入产生视觉上令人信服的结果。我们在野外标记的脸谱（LFW），Helen和CelebA数据集上严格评估了建议的模型，并报告了与现有最​​新技术相比更出色的性能。所提出的C-SRIP模型（具有先验身份的级联超分辨率）能够放大（微小）在不受限制的条件下捕获的低分辨率图像，并为各种低分辨率输入产生视觉上令人信服的结果。我们在野外标记的脸谱（LFW），Helen和CelebA数据集上严格评估了建议的模型，并报告了与现有最​​新技术相比更出色的性能。