Domain Adaptation for Underwater Image Enhancement

Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and obtain outstanding performance. However, these deep methods ignore the significant domain gap between the synthetic and real data (i.e., inter-domain gap), and thus the models trained on synthetic data often fail to generalize well to real-world underwater scenarios. Moreover, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focuses on this problem and thus their techniques often produce visually unpleasing artifacts and color distortions…

• NN
  National Natural Science Foundation of China

  Awards: 62271276, 62271301, 62071266, 61931022
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2022ZD38