Abstract A Free-Ascending Tripod (FAT) was deployed at a water depth of 2100 m to measure the currents and sediment movement at the seafloor. FAT is used to better understand how and where deep-seafloor sediment moves and accumulates. We also use FATs to study deep-sea biology. In the images obtained by the camera, biological animals can hardly be distinguished. In this paper, we use image processing technology to uncover the real deep-sea scene. We propose four methods for improving the underwater image quality. First, we use the deep-sea optical imaging model to determine the properties of water in different sea areas and then remove the haze from underwater images using the underwater dual dark channel model. Next, we remove the footprint of artificial light through halo-estimation devignetting. Then, we obtain the real deep-sea scene color based on the color temperature of the camera and the inherent optical properties of water. Finally, we propose a semi-self-similarity-based super resolution for super-resolving the low-quality images. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.