# PCA-SRGAN: Incremental Orthogonal Projection Discrimination for Face Super-resolution

arXiv: https://arxiv.org/abs/2005.00306 · HTML: https://mlsanity.com/paper/2005.00306/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 10 of 10)

- [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)
- [Robust Real-World Image Super-Resolution against Adversarial Attacks](https://mlsanity.com/paper/2208.00428/) (2022)
- [Super-Resolving Face Image by Facial Parsing Information](https://mlsanity.com/paper/2304.02923/) (2023)
- [Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors](https://mlsanity.com/paper/2210.06002/) (2022)
- [Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse](https://mlsanity.com/paper/2107.13353/) (2021)
- [Face to Cartoon Incremental Super-Resolution using Knowledge Distillation](https://mlsanity.com/paper/2401.15366/) (2024)
- [Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques](https://mlsanity.com/paper/2409.04424/) (2024)
- [AffectSRNet : Facial Emotion-Aware Super-Resolution Network](https://mlsanity.com/paper/2502.09932/) (2025)
- [The Role of Radiographic Knee Alignment in Total Knee Replacement Outcomes and Opportunities for Artificial Intelligence-Driven Assessment](https://mlsanity.com/paper/2508.10941/) (2025)

## Abstract (arXiv)

Generative Adversarial Networks (GAN) have been employed for face super resolution but they bring distorted facial details easily and still have weakness on recovering realistic texture. To further improve the performance of GAN based models on super-resolving face images, we propose PCA-SRGAN which pays attention to the cumulative discrimination in the orthogonal projection space spanned by PCA projection matrix of face data. By feeding the principal component projections ranging from structure to details into the discriminator, the discrimination difficulty will be greatly alleviated and the generator can be enhanced to reconstruct clearer contour and finer texture, helpful to achieve the high perception and low distortion eventually. This incremental orthogonal projection discrimination has ensured a precise optimization procedure from coarse to fine and avoids the dependence on the perceptual regularization. We conduct experiments on CelebA and FFHQ face datasets. The qualitative visual effect and quantitative evaluation have demonstrated the overwhelming performance of our model over related works.