Adaptive imaging through dense dynamic scattering media using transfer learning

Abstract

Imaging through scattering media is a long-standing challenge in optical imaging, holding substantial importance in fields like biology, transportation, and remote sensing. Recent advancements in learning-based methods allow accurate and rapid imaging through optically thick scattering media. However, the practical application of data-driven deep learning faces substantial hurdles due to its inherent limitations in generalization, especially in scenarios such as imaging through highly non-static scattering media. Here we utilize the concept of transfer learning toward adaptive imaging through dense dynamic scattering media. Our approach specifically involves using a known segment of the imaging target to fine-tune the pre-trained de-scattering model. Since the training data of downstream tasks used for transfer learning can be acquired simultaneously with the current test data, our method can achieve clear imaging under varying scattering conditions. Experiment results show that the proposed approach (with transfer learning) is capable of providing more than 5dB improvements when optical thickness varies from 11.6 to 13.1 compared with the conventional deep learning approach (without transfer learning). Our method holds promise for applications in video surveillance and beacon guidance under dense dynamic scattering conditions.