# Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors

arXiv: https://arxiv.org/abs/2210.06002 · HTML: https://mlsanity.com/paper/2210.06002/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors](https://mlsanity.com/paper/1711.10703/) (2017)
- [GANimation: Anatomically-aware Facial Animation from a Single Image](https://mlsanity.com/paper/1807.09251/) (2018)
- [Feedback Network for Image Super-Resolution](https://mlsanity.com/paper/1903.09814/) (2019)
- [Progressive Face Super-Resolution via Attention to Facial Landmark](https://mlsanity.com/paper/1908.08239/) (2019)
- [JhatAA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention](https://mlsanity.com/paper/2003.08834/) (2020)
- [Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation](https://mlsanity.com/paper/2003.13063/) (2020)
- [Attention-Aware Face Hallucination via Deep Reinforcement Learning](https://mlsanity.com/paper/1708.03132/) (2017)
- [EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection](https://mlsanity.com/paper/1702.02925/) (2017)
- [Face Hallucination via Split-Attention in Split-Attention Network](https://mlsanity.com/paper/2010.11575/) (2020)
- [Image Super-Resolution via Dual-State Recurrent Networks](https://mlsanity.com/paper/1805.02704/) (2018)
- [Facial Attribute Capsules for Noise Face Super Resolution](https://mlsanity.com/paper/2002.06518/) (2020)
- [PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution](https://mlsanity.com/paper/2005.00306/) (2020)
- [Region Based Adversarial Synthesis of Facial Action Units](https://mlsanity.com/paper/1910.10323/) (2019)

## Built on by (most cited first, 2 of 2)

- [Teacher-Student Network for Real-World Face Super-Resolution with Progressive Embedding of Edge Information](https://mlsanity.com/paper/2405.04778/) (2024)
- [Collaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification](https://mlsanity.com/paper/2607.28130/) (2026)

## Abstract (arXiv)

The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Recent works have achieved success on this task by utilizing facial priors such as facial landmarks. Most existing methods pay more attention to global shape and structure information, but less to local texture information, which makes them cannot recover local details well. In this paper, we propose a novel recurrent convolutional network based framework for face super-resolution, which progressively introduces both global shape and local texture information. We take full advantage of the intermediate outputs of the recurrent network, and landmarks information and facial action units (AUs) information are extracted in the output of the first and second steps respectively, rather than low-resolution input. Moreover, we introduced AU classification results as a novel quantitative metric for facial details restoration. Extensive experiments show that our proposed method significantly outperforms state-of-the-art FSR methods in terms of image quality and facial details restoration.