  Several supervised networks exist that remove haze information from
underwater images using paired datasets and pixel-wise loss functions. However,
training these networks requires large amounts of paired data which is
cumbersome, complex and time-consuming. Also, directly using adversarial and
cycle consistency loss functions for unsupervised learning is inaccurate as the
underlying mapping from clean to underwater images is one-to-many, resulting in
an inaccurate constraint on the cycle consistency loss. To address these
issues, we propose a new method to remove haze from underwater images using
unpaired data. Our model disentangles haze and content information from
underwater images using a Haze Disentanglement Network (HDN). The disentangled
content is used by a restoration network to generate a clean image using
adversarial losses. The disentangled haze is then used as a guide for
underwater image regeneration resulting in a strong constraint on cycle
consistency loss and improved performance gains. Different ablation studies
show that the haze and content from underwater images are effectively
separated. Exhaustive experiments reveal that accurate cycle consistency
constraint and the proposed network architecture play an important role in
yielding enhanced results. Experiments on UFO-120, UWNet, UWScenes, and UIEB
underwater datasets indicate that the results of our method outperform prior
art both visually and quantitatively.
