# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 2 The Proposed Method
### Multiscale Despecking Network  
#### 2.2.1 Main Network Architecture  
We designed a multiscale despecking network (MSDNet) as \(f_{\theta}(\cdot)\) based on the traditional UNet, which adopts the symmetric encoder-decoder structure. This structure can obtain deep features with different scales. At the same time, the downsampling operations in the encoder part can make the network more lightweight. Our proposed MSDNet consists of a preprocessing block (PB), three encoder blocks (EB), three decoder blocks (DB), and an output block (OB). The architecture and the detailed configuration are presented in Figure 4 and Table 1, respectively. To extract the deep semantic features of speckled images with different scales, the input SAR images are fed to the PB followed by three EBs. Each EB is made up of a downsampling subblock and a dense residual subblock (DRB) described in Section 2.2.2. Downsampling can enlarge the receptive field [40] and augment contextual information extraction, effectively facilitating the recovery of SAR images. Furthermore, memory usage and calculation can also be reduced by using downsampling. Here, different from the traditional UNet, the \(3\times 3\) strided convolution with stride 2 and padding 1 was adopted to implement downsampling with learnable parameters. Unlike pooling operations (e.g., max pooling) in the traditional UNet, the strided convolution can achieve downsampling utilizing all pixels in the sliding window rather than one pixel with the max value. Hence, replacing max pooling in traditional UNet by the strided convolution in our network architecture can enhance the interfeaturTherefore, we can obtain the deep semantic features with four different scales (i.e., \(W\times H\), \(\frac{W}{2}\times\frac{H}{2}\), \(\frac{W}{4}\times\frac{H}{4}\), and \(\frac{W}{8}\times\frac{H}{8}\)). With the features with four different scales obtained by the aforementioned PB and three PBs, three DBs and an OB are responsible for gradually recovering despeckled SAR images. Each DB consists of an upsampling subblock and a DRB with dropout, described in Section 2.2.2. Here, the transposed convolution (TConv) with kernel size 2 and stride 2 in the upsampling subblock is used to enlarge the feature scale. To fuse the shallow and deep features, three skip connections are introduced between PB and OB, EB-1 and DB-1, and EB-2 and DB-2, respectively. This is done by the channelwise concatenation, which can help to reuse the features and exploit the network potential. The shallow features come from the PB (\(W\times H\)), EB-1 (\(\frac{W}{2}\times\frac{H}{2}\)), and EB-2 (\(\frac{W}{4}\times\frac{H}{4}\)). Moreover, they are passed to the OB, DB-1, and DB-2 by the concatenation. The deep features come from EB-3 (\(\frac{W}{8}\times\frac{H}{8}\)). Finally, the OB is employed to convert the concatenated features into despeckled results. Here, we provide an explanation about the parameter selection of Table 1. The kernel size of all convolutions (except for TConv) was set to be \(3\times 3\), which can provide a good tradeoff between network performance and memory footprint. Then, for the common convolutions, to keep the size of the feature map of the input and the output of the