Two-step domain adaptation for underwater image enhancement. (February 2022) - Jiang, Qun; Zhang, Yunfeng; Bao, Fangxun; Zhao, Xiuyang; Zhang, Caiming; Liu, Peide
Highlights: Inspired by transfer learning, we migrate in-air image dehazing to underwater image enhancement. We propose a novel two-step domain adaptation framework for underwater image enhancement, which realizes cross-domain adaptation from the air domain to the underwater domain. Our method is trained on real-world underwater images without utilizing underwater images synthesized with in-air images, which eliminates the dependence on underwater paired data. Abstract: In recent years, underwater image enhancement methods based on deep learning have achieved remarkable results. Since the images obtained in complex underwater scenarios lack a ground truth, these algorithms mainly train models on underwater images synthesized from in-air images. Synthesized underwater images are different from real-world underwater images; this difference leads to the limited generalizability of the training model when enhancing real-world underwater images. In this work, we present an underwater image enhancement method that does not require training on synthetic underwater images and eliminates the dependence on underwater ground-truth images. Specifically, a novel domain adaptation framework for real-world underwater image enhancement inspired by transfer learning is presented; it transfers in-air image dehazing to real-world underwater image enhancement. The experimental results on different real-world underwater scenes indicate that the proposed method produces visually satisfactory results.