  Underwater image quality is affected by fluorescence, low illumination,
absorption, and scattering. Recent works in underwater image enhancement have
proposed different deep network architectures to handle these problems. Most of
these works have proposed a single network to handle all the challenges. We
believe that deep networks trained for specific conditions deliver better
performance than a single network learned from all degradation cases.
Accordingly, the first contribution of this work lies in the proposal of an
iterative framework where a single dominant degradation condition is identified
and resolved. This proposal considers the following eight degradation
conditions -- low illumination, low contrast, haziness, blurred image, presence
of noise and color imbalance in three different channels. A deep network is
designed to identify the dominant degradation condition. Accordingly, an
appropriate deep network is selected for degradation condition-specific
enhancement. The second contribution of this work is the construction of
degradation condition specific datasets from good quality images of two
standard datasets (UIEB and EUVP). This dataset is used to learn the condition
specific enhancement networks. The proposed approach is found to outperform
nine baseline methods on UIEB and EUVP datasets.
