  A main challenge faced in the deep learning-based Underwater Image
Enhancement (UIE) is that the ground truth high-quality image is unavailable.
Most of the existing methods first generate approximate reference maps and then
train an enhancement network with certainty. This kind of method fails to
handle the ambiguity of the reference map. In this paper, we resolve UIE into
distribution estimation and consensus process. We present a novel probabilistic
network to learn the enhancement distribution of degraded underwater images.
Specifically, we combine conditional variational autoencoder with adaptive
instance normalization to construct the enhancement distribution. After that,
we adopt a consensus process to predict a deterministic result based on a set
of samples from the distribution. By learning the enhancement distribution, our
method can cope with the bias introduced in the reference map labeling to some
extent. Additionally, the consensus process is useful to capture a robust and
stable result. We examined the proposed method on two widely used real-world
underwater image enhancement datasets. Experimental results demonstrate that
our approach enables sampling possible enhancement predictions. Meanwhile, the
consensus estimate yields competitive performance compared with
state-of-the-art UIE methods. Code available at
https://github.com/zhenqifu/PUIE-Net.

Computer Vision and Pattern Recognition