AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using
Denoising Diffusion Probabilistic Models
- URL: http://arxiv.org/abs/2208.11284v1
- Date: Wed, 24 Aug 2022 03:13:04 GMT
- Title: AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using
Denoising Diffusion Probabilistic Models
- Authors: Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M Patel
- Abstract summary: Atmospheric turbulence causes significant degradation to image quality by introducing blur and geometric distortion.
Various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed.
Denoising Diffusion Probabilistic Models (DDPMs) have recently gained some traction because of their stable training process and their ability to generate high quality images.
- Score: 64.24948495708337
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although many long-range imaging systems are designed to support extended
vision applications, a natural obstacle to their operation is degradation due
to atmospheric turbulence. Atmospheric turbulence causes significant
degradation to image quality by introducing blur and geometric distortion. In
recent years, various deep learning-based single image atmospheric turbulence
mitigation methods, including CNN-based and GAN inversion-based, have been
proposed in the literature which attempt to remove the distortion in the image.
However, some of these methods are difficult to train and often fail to
reconstruct facial features and produce unrealistic results especially in the
case of high turbulence. Denoising Diffusion Probabilistic Models (DDPMs) have
recently gained some traction because of their stable training process and
their ability to generate high quality images. In this paper, we propose the
first DDPM-based solution for the problem of atmospheric turbulence mitigation.
We also propose a fast sampling technique for reducing the inference times for
conditional DDPMs. Extensive experiments are conducted on synthetic and
real-world data to show the significance of our model. To facilitate further
research, all codes and pretrained models will be made public after the review
process.
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