Underwater Monocular Depth Estimation Based on Physical-Guided Transformer

Owing to the light absorption and wavelength scattering in underwater environments, underwater images are severely degraded, which directly affects the depth estimation of underwater scenes. Accurate underwater depth estimation is essential for representing and understanding underwater scenes. However, the existing underwater depth estimation methods have not fully taken into account the distinctive physical properties of underwater environments, which has resulted in increased bias and feature distortion in the depth estimation results. In this paper, an underwater monocular depth estimation method based on physical-guided Transformer (UPGformer) is proposed, considering the characteristics of underwater imaging, including shallow feature extraction, encoding, decoding, and regression stages. Specifically, in the shallow feature extraction stage, considering the color deviation of underwater images and extracting richer primary features, an enrichment and extraction depth Transformer (EEDT) module is proposed, by interacting physically inverted transmission maps of the underwater dark channel prior (UDCP) with physical color-compensated underwater images through self-attention. In the encoding stage, considering the nonuniform degradation of underwater images (nonuniform local distortion and inconsistent channel degradation), the underwater physical Transformer interaction encoder (UPTE) module, which fuses the Transformer and physically inverted transmission maps, is proposed. Furthermore, in the decoding stage, to better recover features and reduce information loss, the underwater physical embedded decoding (UPED) module is proposed, which embeds the physically inverted transmission maps with the upsampling process. Finally, the depth map is constructed during the regression stage. The experimental results demonstrate that the proposed UPGformer outperforms existing methods, both qualitatively and quantitatively.