  Raw underwater images are degraded due to wavelength dependent light
attenuation and scattering, limiting their applicability in vision systems.
Another factor that makes enhancing underwater images particularly challenging
is the diversity of the water types in which they are captured. For example,
images captured in deep oceanic waters have a different distribution from those
captured in shallow coastal waters. Such diversity makes it hard to train a
single model to enhance underwater images. In this work, we propose a novel
model which nicely handles the diversity of water during the enhancement, by
adversarially learning the content features of the images by disentangling the
unwanted nuisances corresponding to water types (viewed as different domains).
We use the learned domain agnostic features to generate enhanced underwater
images. We train our model on a dataset consisting images of 10 Jerlov water
types. Experimental results show that the proposed model not only outperforms
the previous methods in SSIM and PSNR scores for almost all Jerlov water types
but also generalizes well on real-world datasets. The performance of a
high-level vision task (object detection) also shows improvement using enhanced
images with our model.
