Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of the image patch. In addition, when they are confronted with misalignment or the small sample size (SSS) problem, the hallucination performance is very poor. To this end, this paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL). Under the context-patch-based framework, we advance a thresholding-based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulate the case that the HR version of the input LR face is present in the training set, it thus iteratively enhances the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. In addition, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL.

中文翻译：

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基于阈值局部约束表示和再现学习的上下文补丁人脸幻觉


面部幻觉是一种利用从 HR/LR 面部对学习到的先验知识，从低分辨率 (LR) 面部重建高分辨率 (HR) 面部的技术。大多数最先进的技术利用人脸的位置补丁先验知识来估计每个图像补丁的最佳表示系数。然而，它们只关注位置信息，通常忽略图像块的上下文信息。此外，当他们遇到未对准或小样本量（SSS）问题时，幻觉表现非常差。为此，本文结合图像块的上下文信息，提出了一种强大且高效的基于上下文块的人脸幻觉方法，即阈值局部约束表示和再现学习（TLcR-RL）。在基于上下文补丁的框架下，我们提出了一种基于阈值的表示方法，以提高重建精度并降低计算复杂度。为了进一步提高所提出算法的性能，我们提出了一种称为再现学习的提升策略。通过将估计的HR人脸添加到训练集中，可以模拟输入LR人脸的HR版本存在于训练集中的情况，从而迭代增强最终的幻觉结果。实验表明，所提出的 TLcR-RL 方法在主观和客观上都实现了幻觉结果的大幅增加。此外，所提出的框架对于人脸错位和SSS问题更加鲁棒，并且当LR测试人脸来自现实世界时，其幻觉HR人脸仍然非常好。 MATLAB 源代码可从 https://github.com/junjun-jian/TLcR-RL 获取。