# ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence

arXiv: https://arxiv.org/abs/1910.03119 · HTML: https://mlsanity.com/paper/1910.03119/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 18 of 18)

- [AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2208.11284/) (2022)
- [FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude](https://mlsanity.com/paper/2306.17206/) (2023)
- [Physics-Driven Turbulence Image Restoration with Stochastic Refinement](https://mlsanity.com/paper/2307.10603/) (2023)
- [Learning to restore images degraded by atmospheric turbulence using uncertainty](https://mlsanity.com/paper/2207.03447/) (2022)
- [LTT-GAN: Looking Through Turbulence by Inverting GANs](https://mlsanity.com/paper/2112.02379/) (2021)
- [Multi-Modal Human Authentication Using Silhouettes, Gait and RGB](https://mlsanity.com/paper/2210.04050/) (2022)
- [TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence](https://mlsanity.com/paper/2203.06764/) (2022)
- [Deep Learning Techniques for Atmospheric Turbulence Removal: A Review](https://mlsanity.com/paper/2409.14587/) (2024)
- [Deformation-Invariant Neural Network and Its Applications in Distorted Image Restoration and Analysis](https://mlsanity.com/paper/2310.02641/) (2023)
- [Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors](https://mlsanity.com/paper/2212.07352/) (2022)
- [MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability](https://mlsanity.com/paper/2503.17700/) (2025)
- [Object recognition in atmospheric turbulence scenes](https://mlsanity.com/paper/2210.14318/) (2022)
- [Weakly Supervised Face and Whole Body Recognition in Turbulent Environments](https://mlsanity.com/paper/2308.11757/) (2023)
- [A comparison of different atmospheric turbulence simulation methods for image restoration](https://mlsanity.com/paper/2204.08974/) (2022)
- [DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection](https://mlsanity.com/paper/2507.04323/) (2025)
- [JDATT: A Joint Distillation Framework for Atmospheric Turbulence Mitigation and Target Detection](https://mlsanity.com/paper/2507.19780/) (2025)
- [DaliID: Distortion-Adaptive Learned Invariance for Identification Models](https://mlsanity.com/paper/2302.05753/) (2023)
- [Circular Quasiconformal Deturbulence: Geometry-Based Restoration from Multiple Turbulent Frames](https://mlsanity.com/paper/2504.13432/) (2025)

## Abstract (arXiv)

Image degradation due to atmospheric turbulence is common while capturing images at long ranges. To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image. The disentanglement is achieved by decomposing the distortion due to turbulence into blur and deformation components using deblur generator and deformation correction generator respectively. Two paths of restoration are implemented to regularize the disentanglement and generate two restored images from one degraded image. A fusion function combines the features of the restored images to reconstruct a sharp image with rich details. Adversarial and perceptual losses are added to reconstruct a sharp image and suppress the artifacts respectively. Extensive experiments demonstrate the effectiveness of the proposed restoration algorithm, which achieves satisfactory performance in face restoration and face recognition.