Transformer-based improved U-net for high-performance underwater polarization imaging

Abstract

Polarization imaging plays an important role in underwater imaging technology, as it allows for the suppression of backscattering light and thus enhances the quality of underwater images. Furthermore, with advancements in computer hardware technology, deep learning (DL) has experienced rapid development in recent years. However, existing methods based on convolutional neural networks (CNN) have the limitations of local feature extraction and fail to full use of the salient target’s polarization features. In this paper, we propose a transformer-based improved U-Net (TIU-Net) to further improve the performance of underwater polarization imaging. The proposed TIU-Net leverages CNN and transformer module to extract short-range and long-range features respectively for underwater target reconstruction. Meanwhile, by entering multi-dimensional information, each of which has a different emphasis on the expression of target features, to enhance the performance and stability of the reconstruction model. Experimental results on our established polarization underwater dataset show the superiority of our proposed method for underwater imaging, achieving the efficient imaging and high-quality generalization imaging in highly turbid underwater environments.