Reconstruction of degraded image transmitting through ocean turbulence via deep learning

Abstract

When a laser carrying image information is transmitted in seawater, the presence of ocean turbulence leads to significant degradation of the received information due to the effect of interference. To address this issue, we propose a deep-learning-based method to retrieve the original information from a degraded pattern. To simulate the propagation of laser beams in ocean turbulence, a model of an ocean turbulence phase screen based on the power spectrum inversion method is used. The degraded images with different turbulence conditions are produced based on the model. A Pix2Pix network architecture is built to acquire the original image information. The results indicate that the network can realize high-fidelity image recovery under various turbulence conditions based on the degraded patterns. However, as turbulence strength and transmission distance increase, the reconstruction accuracy of the Pix2Pix network decreases. To further improve the image reconstruction ability of neural network architectures, we established three networks (U-Net, Pix2Pix, and Deep-Pix2Pix) and compared their performance in retrieving the degraded patterns. Overall, the Pix2Pix network showed the best performance for image reconstruction.