# Face Hallucination using Linear Models of Coupled Sparse Support

arXiv: https://arxiv.org/abs/1512.06009 · HTML: https://mlsanity.com/paper/1512.06009/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2015.

## Built on by (most cited first, 9 of 9)

- [Deep Learning-based Face Super-Resolution: A Survey](https://mlsanity.com/paper/2101.03749/) (2021)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Pro-UIGAN: Progressive Face Hallucination from Occluded Thumbnails](https://mlsanity.com/paper/2108.00602/) (2021)
- [From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution](https://mlsanity.com/paper/2108.13584/) (2021)
- [Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior](https://mlsanity.com/paper/2111.10634/) (2021)
- [A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning](https://mlsanity.com/paper/2203.14203/) (2022)
- [Effective Adapter for Face Recognition in the Wild](https://mlsanity.com/paper/2312.01734/) (2023)
- [Exploring Body Texture from mmW Images for Person Recognition](https://mlsanity.com/paper/2203.15618/) (2022)
- [A Unified Framework to Super-Resolve Face Images of Varied Low Resolutions](https://mlsanity.com/paper/2306.03380/) (2023)

## Abstract (arXiv)

Most face super-resolution methods assume that low-resolution and high-resolution manifolds have similar local geometrical structure, hence learn local models on the lowresolution manifolds (e.g. sparse or locally linear embedding models), which are then applied on the high-resolution manifold. However, the low-resolution manifold is distorted by the oneto-many relationship between low- and high- resolution patches. This paper presents a method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold. For this, in a first step, the low-resolution patch is used to derive a globally optimal estimate of the high-resolution patch. The approximated solution is shown to be close in Euclidean space to the ground-truth but is generally smooth and lacks the texture details needed by state-ofthe-art face recognizers. This first estimate allows us to find the support of the high-resolution manifold using sparse coding (SC), which are then used as support for learning a local projection (or upscaling) model between the low-resolution and the highresolution manifolds using Multivariate Ridge Regression (MRR). Experimental results show that the proposed method outperforms six face super-resolution methods in terms of both recognition and quality. These results also reveal that the recognition and quality are significantly affected by the method used for stitching all super-resolved patches together, where quilting was found to better preserve the texture details which helps to achieve higher recognition rates.