# Thermal to Visible Image Synthesis under Atmospheric Turbulence

arXiv: https://arxiv.org/abs/2204.03057 · HTML: https://mlsanity.com/paper/2204.03057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment](https://mlsanity.com/paper/2005.05005/) (2020)
- [Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform](https://mlsanity.com/paper/2107.11627/) (2021)
- [Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition](https://mlsanity.com/paper/1708.02412/) (2017)
- [Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients](https://mlsanity.com/paper/2004.11210/) (2020)
- [Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks](https://mlsanity.com/paper/1812.05155/) (2018)
- [A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset](https://mlsanity.com/paper/2101.02637/) (2021)
- [LTT-GAN: Looking Through Turbulence by Inverting GANs](https://mlsanity.com/paper/2112.02379/) (2021)
- [Polarimetric Thermal to Visible Face Verification via Self-Attention Guided Synthesis](https://mlsanity.com/paper/1904.07344/) (2019)
- [Cross-Domain Identification for Thermal-to-Visible Face Recognition](https://mlsanity.com/paper/2008.08473/) (2020)
- [Cross-Spectral Face Hallucination via Disentangling Independent Factors](https://mlsanity.com/paper/1909.04365/) (2019)
- [Learning to Restore a Single Face Image Degraded by Atmospheric Turbulence using CNNs](https://mlsanity.com/paper/2007.08404/) (2020)
- [Thermal to Visible Synthesis of Face Images using Multiple Regions](https://mlsanity.com/paper/1803.07599/) (2018)
- [Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis](https://mlsanity.com/paper/1901.00889/) (2019)
- [Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN](https://mlsanity.com/paper/2104.06534/) (2021)
- [Deep Cross Polarimetric Thermal-to-visible Face Recognition](https://mlsanity.com/paper/1801.01486/) (2018)

## Built on by (most cited first, 1 of 1)

- [Multi-Attribute guided Thermal Face Image Translation based on Latent Diffusion Model](https://mlsanity.com/paper/2512.21032/) (2025)

## Abstract (arXiv)

In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime conditions. However, such imaging systems often suffer from atmospheric turbulence, which introduces severe blur and deformation artifacts to the captured images. Such an issue is unavoidable in long-range imaging and significantly decreases the face verification accuracy. In this paper, we first investigate the problem with a turbulence simulation method on real-world thermal images. An end-to-end reconstruction method is then proposed which can directly transform thermal images into visible-spectrum images by utilizing natural image priors based on a pre-trained StyleGAN2 network. Compared with the existing two-steps methods of consecutive turbulence mitigation and thermal to visible image translation, our method is demonstrated to be effective in terms of both the visual quality of the reconstructed results and face verification accuracy. Moreover, to the best of our knowledge, this is the first work that studies the problem of thermal to visible image translation under atmospheric turbulence.