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.

翻译：水下图像增强是一项具有挑战性的任务，原因是水下环境引发的复杂图像退化。为解决此问题，以往方法常将退化过程理想化，忽略了介质噪声和物体运动对图像特征分布的影响，限制了模型的泛化能力和适应性。先前方法使用了由原始图像和合成真实图像构建的参考梯度，这可能导致网络性能受到低质量训练数据的影响。本文方法利用预测图像动态更新伪标签，引入动态梯度以优化网络的梯度空间，从而提升图像质量并避免陷入局部最优。此外，我们提出了基于通道组合推断（CCI）策略的特征恢复与重建模块（FRR）以及频域平滑模块（FRS）。这些模块在降低各类噪声对网络性能影响的同时，解耦了其他退化特征。在多个公开数据集上的实验表明，本方法优于现有最先进方法，尤其在UIEB数据集上取得了PSNR为25.6dB、SSIM为0.93的性能里程碑。其在参数量和推理时间方面的效率进一步验证了其广泛实用性。代码将公开发布。