Underwater Image Enhancement Based on Difference Convolution and Gaussian Degradation URanker Loss Fine-Tuning

Underwater images often suffer from degradation such as color distortion and blurring due to light absorption and scattering. It is essential to utilize underwater image enhancement (UIE) methods to acquire high-quality images. Convolutional networks are commonly used for UIE tasks, but their learning capacity is still underexplored. In this paper, a UIE network based on difference convolution is proposed. Difference convolution enables the model to better capture image gradients and edge information, thereby enhancing the network's generalization capability. To further improve performance, attention-based fusion and normalization modules are incorporated into the model. Additionally, to mitigate the impact of the absence of authentic reference images in datasets, a URanker loss module based on Gaussian degradation is proposed during the fine-tuning. The input images are subjected to Gaussian degradation, and the image quality assessment model URanker is utilized to predict the scores of the enhanced images before and after degradation. The model is further fine-tuned using the score difference between the two. Extensive experimental results validate the outstanding performance of the proposed method in UIE tasks.