# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 2 Related Works
### Deep Learning-Based Methods  
With the rapid development of deep learning, some researchers utilize deep learning networks to more accurately estimate the parameters of physical models for underwater image recovery and enhancement. [PERSON] et al. [11] designed a WaterGAN to generate a large dataset of realistic underwater images and corresponding in-air and depth images, and proposed a restoration network for color correction. [PERSON] et al. [12] proposed UIE-net, which consists of two parallel branches to estimate the parameters of an underwater imaging model for enhancement. [PERSON] et al. [8] proposed a network encoding multiple color spaces to diversify the feature representation by fusing features of RGB, Lab, and HSV color spaces into a unified structure. Based on the characteristics of underwater image attenuation, some researchers proposed transmittance estimation for different channels [26]. Their networks can effectively improve visual quality. However, due to the simplified imaging model and heavy reliance on hand-crafted priors, they are not robust in heavy scattering or insufficient-light conditions.  
Model-free deep learning methods [7; 13; 14; 15; 16; 17] directly learn the mapping relationship between a degraded image and its corresponding clear image without an imaging model or prior conditions. [PERSON] et al. [16] utilized CycleGAN to improve the visual quality of underwater images and generated a dataset containing a large number of pairs of images. [PERSON] et al. [17] generated synthetic underwater training data by CycleGAN, introduced the very-deep super-resolution reconstruction model (VDSR) [27] for underwater image enhancement, and proposed underwater resnet (Uresnet). [PERSON] et al. [14] designed a fully data-driven UWCNN model based on the underwater scene prior. It does not need to estimate the parameters of the imaging model. They further [15] proposed a Water-Net model based on a CNN and constructed an enhanced benchmark dataset with 950 underwater images. [PERSON] et al. [10] proposed a real-time underwater image enhancement model, and used global content, color, local texture, and style information to supervise GAN. [PERSON] et al. [28] proposed a lightweight multiple adaptive feature fusion network that reduces the number of parameters by about 94%. [PERSON] et al. [13] proposed an underwater image enhancement method via self-attention and contrastive learning (UIESC). They designed a lightweight self-attention module that can effectively deal with underwater degradation problems, such as atomization, color distortion, and uneven illumination by separating local features and adaptively learning different weights. [PERSON] et al. [7] integrate the RGB and HSV color spaces into a unified network and obtain good results. [PERSON]. [29] considered the problem of underwater image enhancement as two sub-problems, which can be solved by a two-stage network architecture. With this strategy, they tried to address the issues progressively. [PERSON]. [30] also proposed a multi-stage method to address different degrees of unbalanced attenuation. Although most existing model-free deep learning methods attempt to solve the problem of underwater image enhancement with multiple stages progressively, they only supervise the final output of network and ignore the promotion effect of intermediate results on the final enhanced results. Therefore, we propose the PGN, which introduces supervision at multiple stages to guide the network to learn features progressively, and improves the performance of underwater image enhancement.