An Imaging Information Estimation Network for Underwater Image Color Restoration

Computer vision plays an important role in scientific research, resource exploration, and other underwater applications. However, it suffers from the severe color distortion, which is caused by the scattering and absorption of light in the water. In this article, an underwater image color restoration network (UICRN) is proposed to obtain the real color of the image by estimating the main parameters of the underwater imaging model. First, an encoder neural network is applied to extract features from the input underwater image. Second, three independent decoders are used to estimate the direct light transmission map, backscattered light transmission map, and veiling light. Third, the loss functions and the training strategy are designed to improve the performance of restoration. As we know, the learning-based method would require a paired data set for training. An underwater image generation method is also proposed in this article to obtain the data set consisting of color-distorted images and corresponding ground truth. The method combines the inherent optical properties and apparent optical properties with structure information to generate the paired data set. More than 20 000 pairs of underwater images are generated based on the method. Finally, the UICRN method is quantitatively evaluated through various experiments, such as color chart testing in the South China Sea and natural underwater image evaluation. It demonstrates that the UICRN method is competitive with previous state-of-the-art methods in color restoration and robustness.

Automated summary

Computer vision is crucial in scientific research, resource exploration, and underwater applications, but faces color distortion issues. The proposed underwater image color restoration network (UICRN) estimates parameters of the underwater imaging model to achieve real color. By generating a dataset combining optical properties, the UICRN method competes in color restoration and robustness.