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Due to the absorption and scattering effects of the water, underwater imagestend to suffer from many severe problems, such as low contrast, grayed outcolors and blurring content. To improve the visual quality of underwaterimages, we proposed a novel enhancement model, which is a trainable end-to-endneural model. Two parts constitute the overall model. The first one is anon-parameter layer for the preliminary color correction, then the second partis consisted of parametric layers for a self-adaptive refinement, namely thechannel-wise linear shift. For better details, contrast and colorfulness, thisenhancement network is jointly optimized by the pixel-level andcharacteristiclevel training criteria. Through extensive experiments on naturalunderwater scenes, we show that the proposed method can get high qualityenhancement results.

Underwater image enhancement with Image Colorfulness Measure