  In an underwater scene, wavelength-dependent light absorption and scattering
degrade the visibility of images, causing low contrast and distorted color
casts. To address this problem, we propose a convolutional neural network based
image enhancement model, i.e., UWCNN, which is trained efficiently using a
synthetic underwater image database. Unlike the existing works that require the
parameters of underwater imaging model estimation or impose inflexible
frameworks applicable only for specific scenes, our model directly reconstructs
the clear latent underwater image by leveraging on an automatic end-to-end and
data-driven training mechanism. Compliant with underwater imaging models and
optical properties of underwater scenes, we first synthesize ten different
marine image databases. Then, we separately train multiple UWCNN models for
each underwater image formation type. Experimental results on real-world and
synthetic underwater images demonstrate that the presented method generalizes
well on different underwater scenes and outperforms the existing methods both
qualitatively and quantitatively. Besides, we conduct an ablation study to
demonstrate the effect of each component in our network.
