# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 3 Proposed Method
### Multi-Scale Degradation-Representation-Guided Deblurring (Reblurring) Subnetwork
The structure of the feature enhancement module is shown in Figure 4, which consists of four sets of cascaded blocks of the Conv \(3\times 3\) layer and rectified linear unit (ReLU), jump connections, and one Conv \(1\times 1\) layer. As in Figure 3, 64/256 indicates the number of channels. It should be emphasized that the residual skip connection (indicated by a dashed line in the figure) of the input feature only exists at the encoder end, which is caused by the different number of channels between the encoder and decoder ends (256 at the encoder end and only 64 at the decoder end). Figure 5 shows the structure of the enhanced residual bridge connection. This module consists of a cascade of \(N_{i}\) residual blocks and an attention module in the end. Each residual block consists of two Conv \(3\times 3\) layers, one ReLU layer and concatenation. Since the network enters deeper layers when \(i\) increases, the number of differences between the encoder-side features and the decoder-side features decreases, and therefore the number of residual blocks required decreases. In this paper, \(N_{i}\) is set as \(N_{i}=4-i+1\), i.e., 4, 3, 2, and \(1\) residual blocks from top to bottom, respectively. The structure of the attention module is illustrated in Figure 6. Inspired by [25], the X-Y avg/max pool is designed to extract features in two different dimensions (vertical and horizontal directions). In more detail, the features are input and divided into two paths in the X-Y Avg Pool and X-Y Max Pool, followed by the average/max pooling of X (horizontal direction) and Y (vertical direction) in two modules, respectively, and then output via Concat operation. Afterwards, the average pooled and max pooled features are concatenated together again, and then partitioned after the Conv \(1\times 1\) layer, BN (batch normalization) layer and nonlinear layer; finally, the reweighted output is obtained through a Sigmoid function. Figure 7 shows the structure of the multi-scale degradation representation fusion block. Inputs that do not belong to the specific layer are denoted as the inputs of complementary layers. For instance, the complementary layers of the third layer are the first, second and fourth layer. As mentioned in Equation (6) above, the dimensionality of the encoder-side features \(R_{i}\) of this layer and the implicit degradation representation \(E\) are not necessarily the same, so the inputs of the implicit degradation representation \(E\) need to go through interpolation down-sampling and ReLU first. The inputs of the complementary layers also  
Figure 4: Structure of the feature enhancement block. Figure 5: Structure of the enhanced residual bridge connection. need to be scale-transformed accordingly. In summary, the feature inputs of the upper and lower layers need to go through down-sampling/up-sampling, Conv \(3\times 3\), and ReLU, respectively. Scale-transformed \(E\) and \(R_{i}\) share the same dimensions of \(64\times H_{i}\times W_{i}\). Afterwards, they are concatenated together by the Concat operation and fed into enhanced residual bridge connection after a Conv \(1\times 1\) to reduce the number of channels to 64, thus obtaining the corresponding \(R_{i}^{\prime}\) at the decoder end. In summary, with the design of a high-dimensional reconstruction subnetwork detailed above, not only are the features of the input image itself efficiently extracted and fused with  
Figure 6: Structure of attention. Figure 7: Structure of the multi-scale degradation representation fusion block. the decoder-side features, but also the implicit degradation representation \(E\) is incorporated into the obtained image features in various dimensions and utilized several times. The pseudo-code of the entire proposed method is shown in Algorithm 1.