Perception-Driven Deep Underwater Image Enhancement Without Paired Supervision

Underwater image enhancement (UIE) aims to improve the visual quality of raw underwater images. Current UIE algorithms primarily train a deep neural network (DNN) on synthetic datasets or datasets with pseudo labels by minimizing the reconstruction loss between enhanced images and ground truth images. However, there is a domain gap between synthetic and real-world underwater images, and the widely used
$\ell _{1}$
or
$\ell _{2}$
loss tends to overlook the importance of human perception, resulting in unsatisfactory perceptual quality of the final enhanced results. In this paper, we propose an unsupervised perception-driven DNN called PDD-Net for generalizable UIE. Instead of relying on paired images for training, we resort to an unsupervised generative adversarial network (GAN) with a large-scale set of easily available natural images as the target domain. This enables training on larger image sets collected from various domains while avoiding over-fitted to any specific data generation protocol. Additionally, to make the visual quality of enhanced underwater images more in line with human perception, we pre-train a DNN-based pairwise quality ranking (PQR) model based on which a PQR loss is formulated to progressively guides the enhancement of raw underwater image toward the higher quality direction. In addition, we introduce a global attention module (GAM) that integrates modulation and attention mechanisms to enable capturing rich global and local information, leading to improvements in both brightness and contrast. Extensive experiments demonstrate that our proposed PDD-Net exhibits excellent generalization capabilities and outperforms existing methods in terms of both visual perception quality and quantitative indicators across different datasets.