# Haze-Aware Attention Network for Single-Image Dehazing
## 3 Method
### Multiscale Frequency Enhancement Module  
Traditional image restoration methods often focus on enhancing the frequency characteristics of images to improve their clarity and detail. These methods use transformation techniques, such as wavelet and Fourier transforms, to decompose the frequency features of an image into several different frequency bands. The purpose of this is to create isolation between signals of different frequencies, thus reducing their mutual interference, so that each band can be processed independently. Applying different convolutional kernels to these bands can further extract and enhance information within specific frequency ranges. This approach allows for the optimization of high-frequency details and low-frequency contours separately during image processing to achieve better restoration results. However, there are some limitations to this method. Firstly, it may not accurately identify and select the frequency components that carry the most important information in the image. Secondly, the need to process multiple bands separately not only increases the complexity of the algorithm but also significantly raises the computational cost. Especially during the inverse transformation, the operation must be performed separately for each band, which can be a bottleneck when computational resources are limited. Furthermore, since the size of the degradation blur is always variable, the field of view (receptive field) is crucial in the image restoration process. We propose an exceptionally concise and efficient multiscale frequency enhancement module that employs extremely lightweight learnable parameters to effectively decompose frequencies into distinct components, thereby highlighting parts that contain key information. As depicted in Figure 3, we fully considered the impact of the receptive field in our design by utilizing convolutional kernels of sizes \(3\times 3\), \(5\times 5\), and \(7\times 7\) kernels, along with a global kernel, to capture four low-frequency components with different receptive field sizes. By subtracting these low-frequency components from the original input, we were able to generate high-frequency components and enhance the frequency sub-bands carrying significant information through network parameters. Subsequently, we applied these learnable channel weights to different frequency sub-bands. This process not only allows for individual processing of each frequency sub-band but also achieves fine-tuning of features, thus further enhancing the quality of image restoration. The MFEM primarily consists of two main parts: the decoupler and the modulator. The decoupler acquires various frequency sub-bands using multiscale filtering. The modulator then highlights the significant frequency sub-bands with learnable parameters and processes each sub-band individually through learnable parameters on the channel dimension. For any input feature map \(X\in R^{\mathbb{C}\times H\times W}\), we obtain the lowest frequency spectrum through average pooling. Then, by subtracting the low-frequency part from \(X\), we obtain the high-frequency part. To fully capture spectral information from different receptive  
Figure 2: Visual results comparisons on real-world hazy images from the RTTS dataset [46]. Zoom in for best view. fields, we process \(X\) using kernels of sizes \(3\times 3\), \(5\times 5\), and \(7\times 7\) and a global kernel. The formula is as follows:  
\[ \begin{split} X_{g}^{l}&=GAP(X),X_{g}^{h}=X-X_{g}^{l}, \\ X_{3\times 3}^{l}&=Conv_{3\times 3}(X),X_{3\times 3}^{h}=X -X_{3\times 3}^{l},\\ X_{5\times 5}^{l}&=Conv_{5\times 5}(X),X_{5\times 5}^{h} =X-X_{5\times 5}^{l},\\ X_{7\times 7}^{l}&=Conv_{7\times 7}(X),X_{7\times 7}^{h} =X-X_{7\times 7}^{l}.\end{split}  \tag{6}\]  
In this context, \(X_{g}^{l}\) and \(X_{g}^{h}\) represent the global low-frequency and high-frequency sub-bands, respectively.