# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 5 Discussion  
In this section, we discuss the advantages of the research and then examine the limitations associated with the proposed method.  
Firstly, our approach is able to better balance performance and complexity, primarily due to two aspects. On the one hand, DAFEN efficiently addresses the unique complexities of RSI [47] through effective local and global processing, thereby achieving strong performance in RSISR tasks. As illustrated in Figure 12, the visualization of the average feature maps reveals the comprehensive approach of DAFEN. For the local aspect, effective feature extraction and progressive refinement are achieved through three CSLBs, preserving the detail fidelity of all targets, regardless of size. For the global aspect, finer texture features are transferred to deeper DAFEB layers for further reconstruction. This allows different DAFEBs to handle features of varying complexity, contributing to richer detail contours. Meanwhile, the FFEM ensures that the generated images are coherent on a macro level and precise on a micro level by efficiently integrating and enhancing multi-level features both locally and globally. This leads to final generated features with more accurate edges and texture information, which is beneficial for the precise reconstruction of remote-sensing images. On the other hand, DAFEN leverages lightweight convolution and dimensionality reduction, enabling our network to deliver superior performance with lower complexity. Group convolution reduces redundant operations while combining channel shuffle to maintain efficient feature extraction. Additionally, CSLB achieves extremely lightweight computation for both branches through channel split. Furthermore, the forward fusion structure of FFEM is also a highly lightweight design. Even compared with the simple 1 \(\times\) 1 convolution fusion method, it effectively reduces the parameter count by approximately 12 K (as shown in Table 9).  
Secondly, we will discuss the limitations of our method and future work. As can be seen from Table 3, our method achieves superior performance at lower scaling factors, especially at \(\times\)2, but the accuracy improvement of DAFEN is relatively modest at higher scaling factors, such as \(\times\)4. We attribute this to the fact that CNN-based RSISR methods are constrained by the local processing principle of convolutional kernels, which hinders direct interaction between distant pixels in the image, leading to insufficient extraction of feature  
Figure 12: Network average feature maps visualization.  
information at high scaling factors. However, at low scaling factors, the original image details are better preserved, which does not significantly affect the feature extraction performance. Another reason is that the trade-off between low-level and high-level features in our forward fusion structure is not perfect. Although we have demonstrated the rationality and effectiveness of the forward fusion structure in our experiments, such a trade-off may require more precise adjustments for different scaling factors. In the future, we will focus on addressing these limitations. On the one hand, we will further investigate methods to enhance the feature representation capability of the model, such as the hybrid use of different convolutions, exploration of more suitable lightweight convolutions, and finer tuning of the forward fusion structure to achieve optimal performance across various scaling factors. On the other hand, we plan to integrate new technologies, such as the Diffusion Model and Mamba, with CNN to compensate for the shortcomings of a single CNN architecture. Additionally, the scope of the paper is limited to RSISR with bicubic downsampling and does not cover other areas such as blind RSISR, continuous RSISR, or hyperspectral image SR. We will explore how to further integrate our model with other techniques to expand its applicability and encompass more diverse RSISR challenges.