# Physics-constrained GAN boosts OAM correction in ocean turbulence

## TL;DR

A physics-constrained GAN improves OAM wavefront correction in oceanic turbulence, achieving high image quality and purity.

## Contribution

Integrating spatial and spectral constraints in a GAN for OAM correction in ocean turbulence.

## Key findings

- The dual-constraint GAN achieved an SSIM of 0.98, significantly improving image reconstruction.
- Modal purity reached 98.4% with dual constraints, outperforming single-constraint models.
- Power spectral density analysis showed dual constraints had a KL divergence of 0.56, indicating better spectral fidelity.

## Abstract

This study addresses the challenge of improving wavefront correction for Orbital Angular Momentum (OAM) in oceanic turbulence using a physics-constrained Generative Adversarial Network (GAN).

We integrated physical constraints into a deep learning framework to reconstruct degraded input images (SSIM = 0.62). The model was trained with varied loss settings, including a baseline model, spectral constraints (+Spec), and spatial constraints (+Ortho).

The dual-constraint approach (+Ortho+Spec) reached a near-optimal SSIM of 0.98. Ablation studies revealed that while +Ortho boosted modal purity to 95.7%, the dual-constraints achieved 98.4% purity. Power spectral density analysis via KL divergence confirmed the dual-constraints' superiority (KL = 0.56) over the baseline (KL = 2.47).

These results demonstrate that integrating both spatial and spectral constraints effectively optimizes reconstruction, purity, and spectral fidelity, offering a robust solution for OAM correction in underwater optical communication systems.