PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we…

• CA
  China Association for Science and Technology

  Award: 2020QNRC001
• NN
  National Natural Science Foundation of China

  Awards: U1913204, 62236008, 62002014, 61991411, U21B2038, 61931008
• TS
  Taishan Scholar Project of Shandong Province

  Award: tsqn202306079
• NS
  Natural Science Fund for Distinguished Young Scholars of Shandong Province

  Award: ZR2020JQ29
• NK
  National Key Research and Development Program of China

  Award: 2021ZD0112100