# Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement

arXiv: https://arxiv.org/abs/1811.09019 · HTML: https://mlsanity.com/paper/1811.09019/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2018.

## Built on by (most cited first, 14 of 14)

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- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer](https://mlsanity.com/paper/2307.16227/) (2023)
- [A survey on facial image deblurring](https://mlsanity.com/paper/2302.05017/) (2023)
- [Learning to Deblur and Rotate Motion-Blurred Faces](https://mlsanity.com/paper/2112.07599/) (2021)
- [Face Deblurring Based on Separable Normalization and Adaptive Denormalization](https://mlsanity.com/paper/2112.09833/) (2021)
- [Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning](https://mlsanity.com/paper/2204.08307/) (2022)
- [Degradation-Agnostic Statistical Facial Feature Transformation for Blind Face Restoration in Adverse Weather Conditions](https://mlsanity.com/paper/2507.07464/) (2025)

## Abstract (arXiv)

We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.