Secondly, a U-net structure is used to construct the underwater image restoration network, which can learn the relationship between the two domains. The confidence value estimates are sorted and compared with the real probability to continuously optimize the confidence estimation and improve the classification performance of the algorithm. Firstly, this paper proposes an improved confidence estimation algorithm, which uses the number of times a sample is correctly predicted in a continuous period as a confidence estimate. To address these challenges and further improve the quality of underwater image restoration, this work proposes a multi-domain translation method based on domain partitioning. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles.
Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming.
0 kommentar(er)
