PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with
  Dual-Discriminators
Runmin Cong Wenyu Yang Wei Zhang Chongyi Li Chun-Le Guo Qingming Huang Sam Kwong
Due to the light absorption and scattering induced by the water medium,
underwater images usually suffer from some degradation problems, such as low
contrast, color distortion, and blurring details, which aggravate the
difficulty of downstream underwater understanding tasks. Therefore, how to
obtain clear and visually pleasant images has become a common concern of
people, and the task of underwater image enhancement (UIE) has also emerged as
the times require. Among existing UIE methods, Generative Adversarial Networks
(GANs) based methods perform well in visual aesthetics, while the physical
model-based methods have better scene adaptability. Inheriting the advantages
of the above two types of models, we propose a physical model-guided GAN model
for UIE in this paper, referred to as PUGAN. The entire network is under the
GAN architecture. On the one hand, we design a Parameters Estimation subnetwork
(Par-subnet) to learn the parameters for physical model inversion, and use the
generated color enhancement image as auxiliary information for the Two-Stream
Interaction Enhancement sub-network (TSIE-subnet). Meanwhile, we design a
Degradation Quantization (DQ) module in TSIE-subnet to quantize scene
degradation, thereby achieving reinforcing enhancement of key regions. On the
other hand, we design the Dual-Discriminators for the style-content adversarial
constraint, promoting the authenticity and visual aesthetics of the results.
Extensive experiments on three benchmark datasets demonstrate that our PUGAN
outperforms state-of-the-art methods in both qualitative and quantitative
metrics.