# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 1 Introduction  
As global pressure on land-based resources continues to grow, increasing attention is beginning to be focused on the oceans and seas as an important source of natural resources. The oceans cover the vast majority of the Earth's surface, which contains rich biodiversity, energy potential, and mineral resources. However, due to complex factors such as scattering, absorption, and color deviation of light in underwater environments, the quality of underwater scenes is significantly degraded. To address these challenging issues, many methods [1; 2; 3; 4; 5; 6] have been developed to improve degraded image quality. In the beginning, people tended to make more elaborate filters to recreate as many images as possible captured in underwater environments, largely inspired by the theory of the human retina [7]. Later, physical models were often used to mimic the respective complex environments in real scenarios [8]. In addition to this, they also used a priori methods to globally enhance the picture [9]. Image super-resolution (SR) techniques are designed to restore high-resolution (HR) images from low-resolution (LR) images, which can restore the details and clarity of the images and improve the visualization and application of underwater images. Although the image improvement of these methods performs well in some specific contexts, it may suffer from poor performance when dealing with complex, dynamic, and variable real-world images. Over the past few years, advances in deep learning have offered fresh solutions for underwater image SR, allowing researchers to better utilize models such as convolutional neural networks (CNNs) to enhance the visual quality and detail of degraded images. [PERSON] et al. [10] proposed a multipath crossing module that contains both residual and dilation blocks to boost the learning ability of the model and enhance the representationof abstract features. [PERSON] et al. [11] presented a lightweight multi-stage information distillation network, referred to as MSIDN. The MSIDN was designed to strike a better balance between performance and applicability by aggregating locally distilled features from different stages to capture more potent feature representations. [PERSON] et al. [12] utilized a convolutional block attention module (CBAM)) [13] to specify weights for channel features derived from convolutional neural networks (CNNs) with varying receptive field sizes. [PERSON] et al. [14] proposed a progressive frequency interleaved network called PFIN to enhance and restore underwater images, performing effective color bias correction and detail enhancement in underwater images. Despite a large amount of previous research on CNN-based methods, previous studies have encountered challenges related to reconstructing optical artificialities (e.g., color distortions) in underwater images. [PERSON] et al. [15] were the first to integrate the HSV color space with deep learning techniques for color correction, noise reduction in the RGB space, and optimization of luminance and satellite images in the HSV space. [PERSON] et al. [16] proposed a transmission-guided framework that seeks to enhance feature representation by incorporating multi-color features. [PERSON] et al. [17] proposed an enhancement method based on a super-resolution convolutional neural network (SRCNN) and perceptual fusion by combining learned and unlearned methods, which achieved exciting results in underwater image deblurring and color enhancement. However, existing methods have limitations that do not take into account the global information interaction. Nowadays, the transformer is widely used in computer vision with its powerful global modeling capabilities. Similarly, various transformer structures are introduced in underwater image restoration tasks to achieve better restoration results. [PERSON] et al.