# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 1 Introduction  
With the development of underwater exploration technology, remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) are widely applied in underwater tasks. Underwater vision systems are increasingly used to help underwater robots complete close-range operations autonomously, because the data acquired by them have the advantages of low noise, rich details, and strong real-time performance compared with underwater sonar systems. Clear underwater images provide rich visual information that is important for underwater vision-based tasks, such as navigation and target and terrain detection [1, 2, 3, 4]. However, due to the light attenuation and scattering in water, the images taken by underwater vision systems are often degraded by low visibility and color distortion, which seriously affect the performance of underwater visual perception tasks. Thus, it is critical to recover clear images from degraded underwater images for vision-guided underwater robots and ocean engineering. Underwater image enhancement and restoration methods can be mainly divided into traditional methods [5, 6] and deep learning-based methods [7, 8]. Traditional data-based methods enhance the visual effect of underwater images by denoising and contrast adjusting. Due to these methods ignoring the degradation mechanism of images in water,they always cause over-enhancement, under-enhancement, and even artifacts. Traditional physical model-based methods restore degraded images by estimating scene transmission maps and ambient light through underwater imaging models. These methods achieve good underwater image enhancement. However, methods that rely on physical models often result in less-than-ideal visual outcomes because they heavily depend on the precision of the modeling and manually designed prior knowledge. In recent years, deep learning-based methods achieve better performance compared with most traditional methods. Some researchers [9; 10] combine physical models with deep learning to promote the effect of enhancement and restoration by learning imaging parameters or prior knowledge. However, these methods assume that all the color channels of an image have the same transmittance. In the case of complex degradation, this will lead to unpleasant visual results, such as color distortion and artifacts. In contrast, model-free deep learning methods [10] directly learn the mapping relationship between a degraded image and its corresponding clear image without an underwater imaging model or prior conditions. However, existing model-based [11; 12] and model-free deep learning methods [7; 13; 14; 15] only supervise the final output of a network, and the intermediate feature learning process of the network is uncontrollable. These supervision methods ignore the guiding effect of intermediate results on the network learning process, which is not conducive to the progressive learning of useful features. In fact, the intermediate results play an important role in promoting the network's feature representation. Introducing reasonable supervision to the intermediate process of the network can guide the network to learn useful features progressively and stably. According to this idea of supervising the network at multiple stages rather than only at the end of the network, we design an underwater image enhancement network that effectively utilizes the intermediate result information to improve the underwater image enhancement effect. The experiment also proves that our network design idea can help the network learn useful features more accurately and efficiently, and obtain better underwater image enhancement results, especially when the illumination is uneven (Figure 1). Figure 1: Visual comparisons of the enhancement: (**a**) Input, (**b**) UGAN [16], (**c**) Uresnet [17], (**d**) FUnIE_GAN [10], (**e**) Water-Net [15] and (**f**) Ours. Our Model recovers more natural colors with almost no artifacts compared with other methods.