  Underwater image enhancement (UIE) is a challenging task due to the complex
degradation caused by underwater environments. To solve this issue, previous
methods often idealize the degradation process, and neglect the impact of
medium noise and object motion on the distribution of image features, limiting
the generalization and adaptability of the model. Previous methods use the
reference gradient that is constructed from original images and synthetic
ground-truth images. This may cause the network performance to be influenced by
some low-quality training data. Our approach utilizes predicted images to
dynamically update pseudo-labels, adding a dynamic gradient to optimize the
network's gradient space. This process improves image quality and avoids local
optima. Moreover, we propose a Feature Restoration and Reconstruction module
(FRR) based on a Channel Combination Inference (CCI) strategy and a Frequency
Domain Smoothing module (FRS). These modules decouple other degradation
features while reducing the impact of various types of noise on network
performance. Experiments on multiple public datasets demonstrate the
superiority of our method over existing state-of-the-art approaches, especially
in achieving performance milestones: PSNR of 25.6dB and SSIM of 0.93 on the
UIEB dataset. Its efficiency in terms of parameter size and inference time
further attests to its broad practicality. The code will be made publicly
available.
