# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Implementation Details  
To obtain low-resolution (LR) training images, we use bicubic interpolation with scaling factors of \(\times 2\), \(\times 3\), and \(\times 4\) to downscale the high-resolution (HR) images. To enhance the diversity of the training set, data augmentation techniques such as horizontal flipping and random \(90^{\circ}\) rotations are applied. During the training phase, DAFEN employs a batch size of 16, and HR image patches of size 192 \(\times\) 192 are randomly cropped from the HR images. We posit that the selected batch size and patch size values strike an optimal balance, respectively, between training speed and gradient stability, as well as between the preservation of local details and the incorporation of global information. To ensure numerical stability, the pixel range of the input images is scaled to [0, 1]. Our network is optimized using the ADAM [70] optimizer, with \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). The learning rate is set to \(5\times 10^{-4}\) and halved every 200 epochs out of 1000 epochs. This learning rate scheduling strategy aims to balance convergence speed and stability, ensuring that the model avoids local optima while maintaining steady performance improvements. All experiments are implemented using the PyTorch (version 1.10.0) framework and evaluated on an NVIDIA GeForce RTX 3090 GPU (manufactured by NVIDIA Corporation, Santa Clara, CA, USA). In the paper, our network consists of four three-layer DAFEBs. The selection of the number of feature channels follows [46], where DAFEN employs 48 feature channels, while its lightweight variant, DAFEN-S, utilizes 32 feature channels. These configurations offer a well-balanced trade-off between model performance and computational cost. Following [46; 63; 71; 72], SR results are evaluated only on the Y channel in the transformed YCbCr space, using the average Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Additionally, we assess the network's complexity using model parameters and Multi-Adds. Similar to [16; 41; 46], we assume that the size of the query image (HR image) is 1280 \(\times\) 720 for calculating Multi-Adds. Table 1 presents the implementation details and hyperparameter settings of our methods and all the comparative lightweight methods discussed in this paper. Specifically, we elaborate on the common parameters employed in the implementation of our methods and the comparatively lightweight methods, encompassing the optimizer for the generator (Optim_g), beta parameters for the Adam optimizer (betas), learning rate (lr), gamma parameter for the learning rate scheduler (gamma), Loss Function type (Loss type), batch size, patch size, Use horizontal flip (Use_hflip), and Use rotation (Use_rot). To validate our insights into the optimization process and demonstrate the rationality of our hyperparameter selection, we designed a hyperparameter analysis experiment, as detailed in Table 2. Specifically, we conduct experiments with our DAFEN at a scaling factor of \(\times 4\), employing various loss functions, batch sizes, patch sizes, and data augmentation strategies as experimental variants. The results demonstrate that our DAFEN achieves the best performance while also maintaining a balance with model complexity.