School of Artificial Intelligence and Automation,Huazhong University of Science and Technology
The existing underwater image enhancement methods based on deep learning have achieved good results in synthetic underwater images. However, due to the large gap between the simplified synthetic image and the complicated real image, the performance of learning based methods will be significantly degraded when dealing with real images. To solve the problem, a joint underwater image generation and removal (JUIGR) method is proposed. The underwater image is decomposed into a clean background layer and a degraded layer, and the background is restored through cycle consistency loss and adversarial loss. Moreover, the transformation between the real and synthetic image further corrects the color and improves image contrast to achieve a good enhancement effect. Extensive experimental results show that the proposed method is superior to the existing methods in terms of color, texture detail and clarity on the real underwater image dataset.