## 3 Proposed Method
### Hierarchical Feature Fusion  
Features from the PVTv2 have preliminary quality perception ability, which can deal with slight distortion to a certain extent. However, they find it difficult to handle complex distortion; hence, how to effectively use these features is extremely important. A direct processing method is to perform concat operations by upsampling or downsampling features to a unified scale. The effect of this processing method is generally not good, and the features are not fused. The features of different scales contain different information, and the fusion between features can mine more useful information. Based on the above analysis, we propose a hierarchical feature fusion module to cope with this challenge; the diagram of this module is shown in Figure 2. We adjusted the feature scale to always by deconvolution, concat the two features and then use one convolution to reduce the feature channel; finally, we used an attention module to enhance the features and obtain the fused features. Only feature \(\mathbf{F}_{4}\) does not need to be fused with the other features, and \(\mathbf{f}_{4}\) can be obtained directly through the attention operation, the size of \(\mathbf{F}_{4}\) is \(512\times 7\times 7\), and the size of \(\mathbf{f}_{4}\) is \(128\times 7\times 7\). Feature \(\mathbf{F}_{3}\) needs to be fused with the features of \(\mathbf{F}_{4}\), and the size of \(\mathbf{F}_{3}\) is \(320\times 14\times 14\). Initially, the size of \(\mathbf{F}_{4}\) is deconvolved to \(512\times 14\times 14\). After that, the \(\mathbf{F}_{3}\) and \(\mathbf{F}_{4}\) are concated, and the size of the feature is \(832\times 14\times 14\). Then, the number of channels is reduced by a \(1\times 1\) convolution. At last, the attention operation is performed to obtain the fusion feature \(\mathbf{f}_{3}\) of \(\mathbf{F}_{3}\) and \(\mathbf{F}_{4}\), and the size of \(\mathbf{f}_{3}\) is \(32\times 56\times 56\). In the same way, the feature \(\mathbf{F}_{2}\) and \(\mathbf{f}_{3}\) are fused to obtain the fusion feature \(\mathbf{f}_{2}\), and the size of \(\mathbf{f}_{2}\) is \(64\times 28\times 28\); the feature \(\mathbf{F}_{1}\) and \(\mathbf{f}_{2}\) are fused to obtain the fusion feature \(\mathbf{f}_{1}\), and the size of \(\mathbf{f}_{1}\) is \(128\times 14\times 14\). Taking the fusion of features \(\mathbf{F}_{1}\) and \(\mathbf{F}_{2}\) as an example, the whole process can be described by:  
\[\mathbf{f}_{i+1}^{{}^{\prime}}=ConvTan(\mathbf{f}_{i+1}),i=1,2, \tag{1}\]  
\[\mathbf{F}_{i}^{{}^{\prime}}=Conv(\mathbf{f}_{i+1}^{{}^{\prime}}\mathbb{G} \mathbf{F}_{i}),i=1,2, \tag{2}\]  
\[\mathbf{f}_{i}=Attention(\mathbf{F}_{i}^{{}^{\prime}}), \tag{3}\]  
where \(ConvTan\) is the deconvolution operation, \(Conv\) is the convolution operation, and \"\(\mathbb{G}\)\" denotes the concat operation. Among them, the attention module mainly included two important aspects of the feature maps, the channel-domain module and spatial-domain module; the detailed process is shown in Figure 3. The input feature map is \(\mathbf{F}_{i}\in\mathbb{R}^{C\times H\times W}\). Firstly, the channel attention module aimed to explore \"what\" the image is by extruding the spatial dimension of the input feature. Two one-dimensional vectors were obtained by the maximum pooling and average pooling of the input features. The two vectors were forwarded to a shared network containing a multilayer perception and a single-layer hidden layer, and the channel was generated by mapping \(\mathbf{M}_{C}\in\mathbb{R}^{C\times 1\times 1}\). Secondly, the spatial attention module was a channel axis, which squeezes the input features, focusing on the image information as the \"where\". The maximum pooling and average pooling operations were applied to the input features along the channel axis, and they were connected together to obtain the feature map with two channels. A convolution operation was used to obtain \(\mathbf{M}_{S}\in\mathbb{R}^{1\times H\times W}\).