Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between…

• NN
  National Natural Science Foundation of China

  Awards: 61701247, 62071272, 61701245, 62171252, 62001158
• CP
  China Postdoctoral Science Foundation

  Awards: 2021M701903, 2019M660438
• NK
  National Key Research and Development Program of China

  Award: 2020AAA0130000