## 2 Related Work
### Network Lightweight  
In response to the limitations posed by computing resources, researchers have directed their efforts toward crafting lightweight networks without significantly compromising model performance. One prominent contribution was MobileNets, pioneered by [PERSON] et al. [37; 38], which leveraged depth-separable convolutions to construct lightweight neural networks. This approach drastically reduced computational complexity and the number of model parameters. Similarly, [PERSON] et al. [39; 54] introduced ShuffleNet, employing point-by-point group convolution and channel mixing to notably curtail computational overhead while enhancing operational efficiency. In the realm of overscore reconstruction, there is a focus on designing lightweight feature extraction modules to bolster efficiency. [PERSON] et al. [55] devised the cascading residual network (CARN), utilizing packet convolutions to minimize floating-point operations. Additionally, [PERSON] et al. [41] proposed RepVGG, decomposing normal convolutions into multi-branch blocks, enhancing traditional VGG performance across multiple high-end visual tasks to rival the ResNet series. Addressing real-time super-resolution, [PERSON] et al. [31] designed a streamlined topology and a re-parameterized edge-oriented convolution block for super resolution (ECBSR) to expedite inferencing speed. [PERSON] et al. [56] employed linear convolutions and collapsible linear blocks to design super-efficient super resolution (SESR) networks, adaptable for folding and merging during inferencing to balance reconstruction quality and inference time. Moreover, [PERSON] et al. [57] introduced the reparameterization-based lightweight image super-resolution network (RepSCN), incorporating re-parameterized distillation blocks (RepDB), self-calibration distillation blocks (SCDB), and a lightweight coordinate attention mechanism (CAM). This innovation aimed to enhance spatial and channel-level feature representation.  
In recent years, researchers have dedicated themselves to optimizing the lightweight nature of ViTs, striving to adapt them for mobile device suitability [58]. Notable efforts include MobileViT, which adopts a hybrid architecture merging lightweight MobileNet blocks with MHSA blocks [43]. MobileViT2 took it a step further by proposing a separable self-attention method, curbing the secondary computational complexity of MHSA [45]. EfficientFormer introduced a dimensionally consistent design paradigm, pushing the boundaries of latency-performance trade-offs for pure ViTs [42]. Meanwhile, MobileFormer devised an architecture that parallels MobileNet and converters via a two-way bridge [43]. Integrating these efficient architecture choices for lightweight ViTs, [PERSON] et al. [59] pro gressively enhanced the mobility of standard lightweight CNNs, notably MobileNetV3, culminating in the development of RepViT. Despite adopting a MetaFormer structure [60], RepViT is solely composed of convolutional layers, showcasing superior performance and efficiency compared to most advanced lightweight ViTs across various computer vision tasks. Furthermore, [PERSON] et al. [32] proposed a novel lightweight super-resolution architecture called DCTA, tailored for remote sensing applications. This architecture introduces a unique distillation CNN-transformer module (DCTB), ingeniously amalgamating the advantages of CNN and transformer structures in a lightweight manner.