Fusing Learning and Non-Learning: Hybrid CNN-Transformer Cooperative-Competitive Network for Underwater Image Enhancement

Underwater image enhancement (UIE) aims to provide high-quality observations of challenging underwater scenarios, which is of great significance for various broadcast technologies. Extensive non-learning-based and learning-based UIE methods have been presented and applied. However, non-learning-based strategies typically struggle to demonstrate superior generalization capabilities, while learning-based strategies generally suffer from potential over- or under-enhancement due to the lack of sufficient prior knowledge. To address the challenges above, this paper presents a heuristic cooperative-competitive network, termed Co<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net. Specifically, our Co<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net integrates non-learning mechanisms into the deep learning framework to achieve information fusion from explainable prior knowledge and discernible hierarchical features, thereby facilitating promising and reasonable enhancement of degraded underwater images. Furthermore, our Co<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net adopts a hybrid convolutional neural network (CNN)-Transformer architecture, which comprises successive cooperative-competitive modules (Co<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Ms) to achieve adequate extraction, representation, and transmission of both prior knowledge and discernible features. Comprehensive experiments are conducted to demonstrate the superiority and universality of our proposed Co<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Net, and sufficient ablation studies are also performed to reveal the effectiveness of each component within our model. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jixun-dmu/Co2Net</uri>