# Face hallucination using cascaded super-resolution and identity priors

arXiv: https://arxiv.org/abs/1805.10938 · HTML: https://mlsanity.com/paper/1805.10938/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 22 of 22)

- [Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2303.16513/) (2023)
- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond](https://mlsanity.com/paper/2207.14812/) (2022)
- [FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration](https://mlsanity.com/paper/2212.02127/) (2022)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition](https://mlsanity.com/paper/2103.13851/) (2021)
- [Octuplet Loss: Make Face Recognition Robust to Image Resolution](https://mlsanity.com/paper/2207.06726/) (2022)
- [Priors in Deep Image Restoration and Enhancement: A Survey](https://mlsanity.com/paper/2206.02070/) (2022)
- [Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation](https://mlsanity.com/paper/2409.02555/) (2024)
- [HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars](https://mlsanity.com/paper/2203.14863/) (2022)
- [Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition](https://mlsanity.com/paper/2211.15225/) (2022)
- [Frequency Aware Face Hallucination Generative Adversarial Network with Semantic Structural Constraint](https://mlsanity.com/paper/2110.01880/) (2021)
- [AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination](https://mlsanity.com/paper/2111.10591/) (2021)
- [VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots](https://mlsanity.com/paper/2105.14954/) (2021)
- [Continuous Facial Motion Deblurring](https://mlsanity.com/paper/2207.06626/) (2022)
- [PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques](https://mlsanity.com/paper/2211.08864/) (2022)
- [SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations](https://mlsanity.com/paper/2308.08884/) (2023)
- [Learning Unified Representations for Multi-Resolution Face Recognition](https://mlsanity.com/paper/2310.09563/) (2023)
- [Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation](https://mlsanity.com/paper/2409.02049/) (2024)
- [Collaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification](https://mlsanity.com/paper/2607.28130/) (2026)

## Abstract (arXiv)

In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors. We approach the problem 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 images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from competing super-resolution approaches that typically 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. Our model is able to upscale (very) low-resolution images captured in unconstrained conditions and produce visually convincing results. We rigorously evaluate the proposed model on a large datasets of facial images and report superior performance compared to the state-of-the-art.