## 2 Related Work
### Frequency Domain in Deep Learning  
Spatial and frequency domain analyses are two primary tools in signal processing. Consequently, spatial and frequency methods are essential in digital image processingdownstream tasks. In recent years, Convolutional Neural Networks (CNNs) have demonstrated remarkable performance improvements in various tasks within digital image processing. However, CNNs excel at spatial feature extraction, which essentially uses spatial domain analysis methods for signal processing. In contrast, frequency domain feature extraction [24] can better capture texture information, providing significant assistance in tasks such as remote sensing image detection and segmentation. Frequency domain methods can enhance specific frequencies while suppressing others, enabling spatial filters to exhibit selectivity more easily. Combining frequency domain analysis methods with CNN convolutions is efficient because explicit selection of specific frequencies is challenging within CNNs. In recent years, scholars have explored integrating frequency domain analysis methods into the field of deep learning, achieving success in areas such as image fusion [25], image compression [26], model pruning [27], image super-resolution [28] and so on. [PERSON] [18] proposed a channel attention model in the frequency domain. By incorporating additional frequency components, this model effectively retains more high-frequency features, thereby addressing the deficiencies observed in channel attention mechanisms, which are characterized by inadequate feature representation. In comparison to DCT, Discrete Wavelet Transform (DWT) [29] has an advantage in capturing edges and other sharp features in images while also being able to represent images in a multi-resolution manner. This makes it more suitable for tasks in remote sensing image processing. Reference [30] combines the features extracted by DWT with those extracted by the backbone for image classification tasks. Reference [31] proposes a wavelet pooling algorithm to replace existing pooling operations. This method decomposes features into a second-level decomposition and retains only low-frequency sub-band information to reduce feature dimensions, thereby addressing overfitting issues associated with max pooling. WACNN [32] filters out irrelevant information in the low-frequency components by using DWT to decompose feature maps, preserving features containing low-frequency components. Simultaneously, discarding only a portion of high-frequency components allows the integration of detailed information from the high-frequency domain, achieving a Noise-Robust effect. WaveC-Nets [33] is designed for classification tasks. The down-sampling operation is replaced with the low-frequency components extracted by DWT. Furthermore, all high-frequency components are discarded, as random noise primarily occurs in those components. Dual Wavelet Attention [34] proposes the Wavelet Channel Attention (WCA) and Wavelet Spatial Attention (WSA). WCA treats low-frequency and high-frequency statistical features as channel scalars, effectively representing the importance of different channels. WSA incorporates high-frequency components to better capture critical structural features in the feature map. Compared to traditional spatial attention, Wavelet Spatial Attention demonstrates superior performance. MFFN [35] proposes a double-backbone network for SAR image object detection tasks, where one backbone extracts semantic information. Furthermore, the other backbone adds frequency features extracted by DWT to enrich texture information. Subsequently, the semantic and frequency features are fused to enhance the detector's performance. After decomposing noisy images using 2D-DWT, we found that a significant amount of noise was concentrated in the diagonal high-frequency component. In contrast, the horizontal and vertical high-frequency components primarily contained contour and detail information, with only minimal noise presence.