# Facial Attribute Capsules for Noise Face Super Resolution

arXiv: https://arxiv.org/abs/2002.06518 · HTML: https://mlsanity.com/paper/2002.06518/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 13 of 13)

- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution](https://mlsanity.com/paper/2204.08696/) (2022)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network](https://mlsanity.com/paper/2407.19768/) (2024)
- [Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution](https://mlsanity.com/paper/2205.03777/) (2022)
- [HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network](https://mlsanity.com/paper/2203.10699/) (2022)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)
- [FaceFormer: Scale-aware Blind Face Restoration with Transformers](https://mlsanity.com/paper/2207.09790/) (2022)
- [Improving Super-Resolution Performance using Meta-Attention Layers](https://mlsanity.com/paper/2110.14638/) (2021)
- [Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors](https://mlsanity.com/paper/2210.06002/) (2022)
- [FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution](https://mlsanity.com/paper/2506.14121/) (2025)
- [Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior](https://mlsanity.com/paper/2205.14377/) (2022)
- [Attention-Guided Multi-scale Interaction Network for Face Super-Resolution](https://mlsanity.com/paper/2409.00591/) (2024)

## Abstract (arXiv)

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.