# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 1 Introduction
[18] proposed a structure with unique hierarchical segmentation and multiple levels based on a transformer, which overcame the challenge of processing large-size images and effectively captured information regarding long-range dependencies. [PERSON] et al. [19] were the first to apply the U-shaped structure in combination with a transformer to UIE and achieved exciting results. A dual attention transformer-based method was introduced by [PERSON] et al. [20] for underwater image enhancement that can better reconstruct the image. [PERSON] et al. [21] offered the SwinIR method for image restoration using the swin transformer to achieve good image reconstruction. [PERSON] et al. [22] proposed a new adaptive group attention method which was added to the transformer to mitigate the diffusion due to underwater magnesium. [PERSON] et al. [23] used air images to guide underwater image clearness and leveraged the transformer to acquire overall information from the underwater images. Although all of the above methods have yielded some results, they all have certain drawbacks. Models based on CNN approaches may require a large number of parameters and computational resources, and deploying such models on resource-constrained underwater devices may be challenging, which happens to be one of the advantages of transformers. Similarly, transformer-based models are based on a self-attentive mechanism that pays global attention to the entire input sequence. In some underwater scenarios, images may contain a large number of local structures and details, and transformers may not be as effective as methods such as CNNs in dealing with these local structures. To address these issues, we propose an active dual-aware integrated network (DAIN) for underwater image SR in this paper. The general architecture can be seen in Figure 1, which shows that the DAIN mainly comprises a sequence of dual-aware enhancement modules (DAEMs). A multi-scale color correction block (MCCB) and swin transformer layer (STL) constitute the DAEM, which can utilize the short-range ability of the CNN and the long-range ability of the STL to better integrate local and global information, thereby improving the temporal resolution of degraded underwater images. The MCCB utilizes multiple channels to process the different colors of underwater images to reduce color bias, while the STL captures global contextual information to improve image detail reconstruction. Experimental results display that our DAIN outperforms the most popular methods with low model capacity. To sum up, our main contributions can be summarized as being three-fold:  
* We introduce a lightweight and efficient DAIN tailored for the underwater SR domain. Leveraging the DAME, which consists of an MCCB and STL, our approach demonstrates superior reconstruction performance, outperforming the majority of existing underwater SR methods. * Our proposed DAEM adeptly integrates local and global features, enhancing the model's capacity to capture intricate details. * The MCCB employs multi-channel and multi-scale strategies to address the challenge of uneven color attenuation in underwater images. Notably, the incorporation of the attention mechanism further elevates the representational prowess of the network. While transformer-based methods excel in global modeling, they exhibit limitations in local modeling. Furthermore, to streamline computations and enhance model efficiency, transformers often entail a significant reduction in computational parameters. Consequently, recent efforts have explored hybrid models that synergize global features extracted by the transformer with local features extracted by a CNN.