# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 5 Conclusions  
We propose an underwater image enhancement method that utilizes a Pixel-Wise Attention Module to introduce reasonable supervision to progressively improve the network's feature representation ability, and finally achieve a better effect. The proposed method breaks down the image restoration process into several controllable sub-processes. By supervising and utilizing multiple intermediate processes, it restores degraded underwater images progressively. Secondly, we propose a Pixel-Wise Attention Module that weighs the image features with fine granularity, optimizing the restoration features, thereby accomplishing the task of underwater image restoration. Experiments indicate that the proposed method can effectively recover the distorted colors and texture details of underwater images, completing the task of underwater image restoration. The idea of introducing supervision in the intermediate process of the network and guiding the network to learn useful features progressively has potential applications in other image recovery tasks, such as denoising and inpainting. Conceptualization, H.J. ; data curation, B.F. and Z.Z. ; formal analysis, H.J. and Q.W. ; funding acquisition, Y.T. ; investigation, Q.W. ; methodology, Q.W. ; project administration, Q.W. and Y.T. ; resources, Y.T. ; software, H.J. and Z.Z. ; supervision, Y.T. ; validation, B.F.; visualization, H.J. and Z.Z. ; writing--review editing, Y.T. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant No. 62073205) and the Project of the Education Department of Liaoning Province (Grant No. IJKMZ2021825). Not applicable. The EUVP dataset is an open dataset accessed on February 18 2020 and can be downloaded at [[https://doi.org/10.1109/LRA.2020.2974710](https://doi.org/10.1109/LRA.2020.2974710)]([https://doi.org/10.1109/LRA.2020.2974710](https://doi.org/10.1109/LRA.2020.2974710)). The UGAN dataset is an open dataset accessed on 28 May 2018 and can be downloaded at [[https://doi.org/10.1109/ICRA.2018.846](https://doi.org/10.1109/ICRA.2018.846)]([https://doi.org/10.1109/ICRA.2018.846](https://doi.org/10.1109/ICRA.2018.846)) 0552. The UIEB dataset is an open dataset accessed on January 11 2019 and can be downloaded at [[https://doi.org/10.1109/TIP.2019.2955241](https://doi.org/10.1109/TIP.2019.2955241)]([https://doi.org/10.1109/TIP.2019.2955241](https://doi.org/10.1109/TIP.2019.2955241)). The C60 dataset is an open dataset accessed on 28 November  
Figure 10: Variation trend of PSNR with an increasing number of FRBs under different network structures. With our effective supervision strategy, as the number of FRBs increases, the network deepens progressively, learns more useful features, and obtains better performance. 2019 and can be downloaded at [[https://doi.org/10.1109/TIP.2019.2955241](https://doi.org/10.1109/TIP.2019.2955241)]([https://doi.org/10.1109/TIP.2019.2955241](https://doi.org/10.1109/TIP.2019.2955241)). The U45 dataset is an open dataset accessed on 17 June 2019 and can be downloaded at [[https://arxiv.org/abs/1906.06819](https://arxiv.org/abs/1906.06819)]([https://arxiv.org/abs/1906.06819](https://arxiv.org/abs/1906.06819)). The authors declare no conflicts of interest. * _[PERSON] (1990)_ [PERSON] Computer modeling and the design of optimal underwater imaging systems. _IEEE J. Ocean. Eng._**1990**, _15_, 101-111. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON]; [PERSON]; [PERSON] Single Image Haze Removal using Dark Channel Prior. _IEEE Trans. Pattern Anal. Mach. Intell._**2011**, _33_, 2341-2353. [CrossRef] [PubMed]
* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON] Single Underwater Image Restoration Using Adaptive Attenuation-Curve Prior. _IEEE Trans. Circuits Syst. I Regul. Pap._**2018**, _65_, 992-1002. [CrossRef]
* _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON] Accurate Image Super-Resolution Using Very Deep Convolutional Networks.