## 7 Conclusions and Future Work  
This work proposes a Lightweight Parallel Strategy Network for underwater image enhancement. By introducing a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block, we addressed the mixed degeneracies of color distortion and the loss of detail in underwater raw images. LPS-Net utilizes these components as the building blocks of parallel branches and employs Gated Fusion Units to fuse features from different branches. Extensive experiments demonstrated that LPS-Net achieved state-of-the-art underwater image enhancement performance in terms of visual quality and evaluative metrics, while using only 80.12 k parameters. The success of LPS-Net proves that tackling mixed degradations by dividing them into sub-problems has the potential of avoiding the use of complex modules and reducing the amount of computation and parameters. Despite the superiority of LPS-Net, there is still room for improvement. Looking ahead, we plan to incorporate frequency domain operations into LPS-Net since processing information in the Fourier space is capable of capturing the global representation in the frequency domain, while normal convolution focuses on learning local representations in the spatial domain. Another direction is to take advantage of both visual transformers and CNNs. It is worth investigating how to bring the merits of transformers while achieving a good balance between UIE performance and model complexity. The proposed LPS-Net demonstrated impressive performance in real-world underwater image enhancement, making it beneficial for other downstream vision applications, such as underwater photography, underwater archeology, and marine biological survey. We also plan to investigate whether LPS-Net can be adapted to other image-restoration tasks [58], such as image dehazing and image deraining. Of course, efforts should be made to tackle mixed degradations in these tasks by dividing them into sub-problems that could be effectively addressed. Conceptualization, J.J.; methodology, J.J. and L.T. ; software, P.H. and J.Y. ; validation, J.J. and J.Y. ; formal analysis, E.C. and P.H. ; investigation, E.C., J.Y. and J.J.; resources, E.C. ; data curation, J.J.; writing--original draft preparation, J.J., L.T. and E.C. ; project administration, E.C. ; funding acquisition, E.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Natural Science Foundation of Fujian Province of China under Grant (2021)01867), Xiamen Ocean and Fisheries Development Special Funds (22 CZB013 HJ04), and the Youth Science and Technology Innovation Program of Xiamen Ocean and Fisheries Development Special Funds (23 ZHZB039 QCB24). Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://drive.google.com/file/d/12W_kkblc2](https://drive.google.com/file/d/12W_kkblc2) Vryb92 HQ6 BfGQ_NKUfZYk13/view]([https://drive.google.com/file/d/12W_kkblc2](https://drive.google.com/file/d/12W_kkblc2) Vryb92 HQ6 BfGQ_NKUfZYk13/view) (accessed on 20 October 2022), [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) CA-8 CzajnVEL4 feBRKdByzEefhw]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) CA-8 CzajnVEL4 feBRKdByzEefhw) ql6Z7/view (accessed on 20 October 2022), [[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) Ew_r83 nXzVk0 hlkfu]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) Ew_r83 nXzVk0 hlkfu) omWqASluxqq6 kaN4/view (accessed on 20 October 2022), and [[https://github.com/IPNUISTlegal/](https://github.com/IPNUISTlegal/)]([https://github.com/IPNUISTlegal/](https://github.com/IPNUISTlegal/)) underwater-test-dataset-U45- (accessed on 20 October 2022).