Abstract
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 a deblur generator and a deformation correction generator, respectively. Two paths of restoration are implemented to regularize the disentanglement and generate two restored images from a given 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 for face restoration and face recognition tasks. © 2021 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 240-251 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Biometrics, Behavior, and Identity Science |
| Volume | 3 |
| Issue number | 2 |
| Online published | 9 Feb 2021 |
| DOIs | |
| Publication status | Published - Apr 2021 |
| Externally published | Yes |
Funding
This work was supported in part by the Office of the Director of National Intelligence (ODNI), and in part by Intelligence Advanced Research Projects Activity (IARPA), through IARPA Research and Development under Contract 2019- 022600002.
Research Keywords
- face recognition
- face verification
- generative adversarial networks
- Image restoration
- turbulence mitigation