Research on Underwater Image Restoration Technology Based on Multi-Domain Translation

Abstract

Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. 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 image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. 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. 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. 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. Secondly, a U-net structure is used to construct the underwater image restoration network, which can learn the relationship between the two domains. The discriminator uses full convolution to improve the performance of the discriminator by outputting the true and false images along with the category to which the true image belongs. Finally, the improved confidence estimation algorithm is combined with the discriminator in the image restoration network to invert the labels for images with low confidence values in the clean domain as images in the degraded domain. The next step of image restoration is then performed based on the new dataset that is divided. In this way, the multi-domain conversion of underwater images is achieved, which helps in the recovery of underwater images. Experimental results show that the proposed method effectively improves the quality and quantity of the images.