Target Oriented Perceptual Adversarial Fusion Network for Underwater Image Enhancement

Due to the refraction and absorption of light by water, underwater images usually suffer from severe degradation, such as color cast, hazy blur, and low visibility, which would degrade the effectiveness of marine applications equipped on autonomous underwater vehicles. To eliminate the degradation of underwater images, we propose a target oriented perceptual adversarial fusion network, dubbed TOPAL. Concretely, we consider the degradation factors of underwater images in terms of turbidity and chromatism. And according to the degradation issues, we first develop a multi-scale dense boosted module to strengthen the visual contrast and a deep aesthetic render module to perform the color correction, respectively.…

• NN
  National Natural Science Foundation of China

  Awards: 61733002, 61922019
• FR
  Fundamental Research Funds for the Central Universities