Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network

Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene. To address the above-mentioned problems, we propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile,…

• NN
  National Natural Science Foundation of China

  Award: 61701245
• PA
  Priority Academic Program Development of Jiangsu Higher Education Institutions
• SF
  Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology

  Award: 2243141701030