# Multi-Module Combination for Underwater Image Enhancement
## 5 Conclusions  
In this paper, a multi-module combination of underwater image enhancement algorithm UMMC is proposed to address the problems of the color distortion, low visibility and low brightness of underwater images. Our approach involves the development of a new network designed to improve enhancement performance through five key modules: the color detection module, the color and white balance correction module, the visibility restoration module, the contrast enhancement module, and the image fusion module. After experimental validation, the method proposed in this paper effectively addresses the issues of color cast and low brightness, making it highly suitable for enhancing real underwater images. Furthermore, our model not only improves image quality but also operates with a faster runtime.  
Although UMMC has achieved significant results, there are still several limitations that need to be addressed. For the issue of supersaturation in images with low levels of chromatic aberration, as well as in foggy images that are typically processed, it is essential to optimize algorithms and explore new solutions in the future. In addition, the setting of \(K\) is also limiting to some extent, and as the next step, we will look for new ways to address this shortcoming such as introducing an adaptive threshold that depends on the image statistics, \(K\). This optimization will be particularly beneficial in addressing a wider range of underwater image challenges. In the future, we plan to adopt a combined approach with deep learning to explore real-time processing and optimization in low-resource environments.  
Z.J.: writing--reviewing and editing, writing--original draft, validation, supervision, software, methodology, survey, funding acquisition, formal analysis, data organization, conceptualization. H.W.: writing--review and editing, writing--original draft, visualization, validation, software, methodology, survey, data curation, conceptualization. G.H.: data organization, investigation, resources, supervision, validation, writing--review and editing. J.C.: data organization, investigation, supervision, visualization. W.F.: conceptualization, funding acquisition, investigation, resources, supervision, writing--review and editing. G.L.: funding acquisition, investigation,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Platform** & **\#Param.** & **FLOPs** & **Time (s)** & **PSNR** & **SSIM** \\ \hline UW-AAE & TensorFlow & 148.77 M & 2805.34 G & 6.97 & 17.12 & 0.85 \\ EUICCCLF & MATLAB & - & - & 0.02 & 16.82 & 0.71 \\ MLLE & MATLAB & - & - & 0.08 & 16.26 & 0.64 \\ PCDE & MATLAB & - & - & 0.16 & 15.13 & 0.60 \\ UWCNN & PyTorch & 0.04 M & 2.61 G & 0.12 & 11.08 & 0.30 \\ UIESS & PyTorch & 4.2 M & 26.35 G & 0.43 & 23.37 & 0.73 \\ BRUIE & MATLAB & - & - & 0.15 & 15.13 & 0.60 \\ UMMC & MATLAB & - & - & 0.07 & 18.03 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of parameters, FLOPs and runtime on different UIE methods. The best result is in red, whereas the second-best result is in green.  
resources, supervision--reviewing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shanghai Municipal Industrial Collaborative Innovation Science and Technology Project [XTCX-KJ-2023-2-15], the National Key Research and Development Program of China (grant number 2022 YFD2401100), and a project jointly funded by the Guangdong Provincial Department of Natural Resources and the Guangdong Provincial Natural Resources Research Committee (grant number [2024]52).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to copyright issues with co-developers.  
The authors declare no conflict of interest.