# Frequency-Separated Attention Network for Image Super-Resolution
###### Abstract  
The use of deep convolutional neural networks has significantly improved the performance of super-resolution. Employing deeper networks to enhance the non-linear mapping capability from low-resolution (LR) to high-resolution (HR) images has inadvertently weakened the information flow and disrupted long-term memory. Moreover, overly deep networks are challenging to train, thus failing to exhibit the expressive capability commensurate with their depth. High-frequency and low-frequency features in images play different roles in image super-resolution. Networks based on CNNs, which should focus more on high-frequency features, treat these two types of features equally. This results in redundant computations when processing low-frequency features and causes complex and detailed parts of the reconstructed images to appear as smooth as the background. To maintain long-term memory and focus more on the restoration of image details in networks with strong representational capabilities, we propose the Frequency-Separated Attention Network (FSANet), where dense connections ensure the full utilization of multi-level features. In the Feature Extraction Module (FEM), the use of the Res ASPP Module expands the network's receptive field without increasing its depth. To differentiate between high-frequency and low-frequency features within the network, we introduce the Feature-Separated Attention Block (FSAB). Furthermore, to enhance the quality of the restored images using heuristic features, we incorporate attention mechanisms into the Low-Frequency Attention Block (LFAB) and the High-Frequency Attention Block (HFAB) for processing low-frequency and high-frequency features, respectively. The proposed network outperforms the current state-of-the-art methods in tests on benchmark datasets. densed by: 28 March 2024  
9 May 2024  
15 May 20241  
and training difficulties. VDSR [6] focuses on residual learning of the differences between HR and LR images, further increasing the network depth. Additionally, the introduction of recursive modules [7; 8] and memory modules [7] also enhances depth. FSRCNN [9] increases network depth by reducing the size of filters. FSRCNN [9] and ESPCN [10] introduced deconvolution and sub-pixel convolution layers, respectively, a structure later adopted by most networks. [PERSON] et al. [11] and [PERSON] et al. [12] further increased network depth using residual blocks, while [PERSON] et al. [13] and [PERSON] et al. [14; 15] introduced data particle geometrical divide algorithms to the field of super-resolution. The authors of [16] added numerous skip connections to mitigate information flow attenuation in overly deep networks. To enhance the network's ability to process high-frequency information, [PERSON] et al. [17] employed deep convolutional networks and ResNet for high-frequency information in images, while [PERSON] et al. [18] identified high-frequency features and increased their learning rates to improve the recovery of complex details in images. Due to the optimal loss function for super-resolution problems being \(L_{1}\), resulting in minimal loss values, the combination of small loss values and excessive layers in deep networks can lead to gradient vanishing. Consequently, it becomes challenging to effectively translate the potential expressive capabilities gained from increased network depth into the model's ability to learn the non-linear mapping from LR to HR images. Images contain high-frequency information representing texture details and low-frequency information describing object edges; similarly, features extracted by the network from LR images also consist of low- and high-frequency features. The use of numerous skip connections for dense connectivity allows for the full, adaptive integration of features across layers, and the use of memory modules achieves persistent memory, mitigating the effects of information flow attenuation caused by network depth. High-frequency features require more complex processing for detailed restoration compared to low-frequency features.