In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, multi-view 3D reconstruction has once again become a hot spot in computer vision. Incremental Structure From Motion (SFM) is currently the most prevalent reconstruction pipeline, but it still faces challenges in reconstruction efficiency, accuracy, and feature matching. In this paper, we use deep learning algorithms for feature matching to obtain more accurate matching point pairs. Moreover, we adopted the improved Gauss–Newton (GN) method, which not only avoids numerical divergence but also accelerates the speed of bundle adjustment (BA). Then, the sparse point cloud reconstructed by SFM and the original image are used as the input of the depth estimation network to predict the depth map of each image. Finally, the depth map is fused to complete the reconstruction of dense point clouds. After experimental verification, the reconstructed dense point clouds have rich details and clear textures, and the integrity, overall accuracy, and reconstruction efficiency of the point clouds have been improved.

This work introduces a two-stage framework, named the Distorted underwater image Restoration Network (DR-Net), to address the complex degradation of underwater images caused by turbulence, water flow fluctuations, and optical properties of water. The first stage employs the Distortion Estimation Network (DE-Net), which leverages a fusion of Transformer and U-Net architectures to estimate distortion information from degraded images and focuses on image distortion recovery. Subsequently, the Image Restoration Generative Adversarial Network (IR-GAN) in the second stage utilizes this estimated distortion information to deblur images and regenerate lost details. Qualitative and quantitative evaluations on both synthetic and real-world image datasets demonstrate that DR-Net outperforms traditional methods and restoration strategies from different perspectives, showcasing its broader applicability and robustness. Our approach enables the restoration of underwater images from a single frame, which facilitates the acquisition of marine resources and enhances seabed exploration capabilities.