Affiliation:
1. Key Laboratory of Atmospheric Optics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
2. Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China
3. Advanced Laser Technology Laboratory of Anhui Province, Hefei 230037, China
4. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
Abstract
Turbulence-degraded image frames are distorted by both turbulent deformations and space–time varying blurs. Restoration of the atmospheric turbulence-degraded image is of great importance in the state of affairs, such as remoting sensing, surveillance, traffic control, and astronomy. While traditional supervised learning uses lots of simulated distorted images for training, it has poor generalization ability for real degraded images. To address this problem, a novel blind restoration network that only inputs a single turbulence-degraded image is presented, which is mainly used to reconstruct the real atmospheric turbulence distorted images. In addition, the proposed method does not require pre-training, and only needs to input a single real turbulent degradation image to output a high-quality result. Meanwhile, to improve the self-supervised restoration effect, Regularization by Denoising (RED) is introduced to the network, and the final output is obtained by averaging the prediction of multiple iterations in the trained model. Experiments are carried out with real-world turbulence-degraded data by implementing the proposed method and four reported methods, and we use four non-reference indicators for evaluation, among which Average Gradient, NIQE, and BRISQUE have achieved state-of-the-art effects compared with other methods. As a result, our method is effective in alleviating distortions and blur, restoring image details, and enhancing visual quality. Furthermore, the proposed approach has a certain degree of generalization, and has an excellent restoration effect for motion-blurred images.
Funder
National Natural Science Foundation of China
Foundation of Advanced Laser Technology Laboratory of Anhui Province
Foundation of Key Laboratory of Science and Technology Innovation of Chinese Academy of Sciences
Subject
General Earth and Planetary Sciences
Reference50 articles.
1. Detecting and tracking moving objects in long-distance imaging through turbulent medium;Chen;Appl. Opt.,2014
2. Online spatio-temporal action detection in long-distance imaging affected by the atmosphere;Chen;IEEE Access,2021
3. Roggemann, M.C., Welsh, B.M., and Hunt, B.R. (1996). Imaging Through Turbulence, CRC Press.
4. Attainment of diffraction limited resolution in large telescopes by Fourier analysing speckle patterns in star images;Labeyrie;Astron. Astrophys.,1970
5. Iterative blind deconvolution method and its applications;Ayers;Opt. Lett.,1988