# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
## 2 SAR Image Denoising Algorithm Based on Self-Learning Strategy DnCNN
### Network Structure  
DnCNN is designed to predict the residual image (noise) from a noisy input image. The residual image is the difference between the noisy image and the clean image. The self-learning strategy in DnCNN involves training the network to learn the mapping from noisy images to their residuals, which can then be subtracted from the noisy image to recover the clean image. In the context of DnCNN, analyzing the histogram of the residual image could help understand the characteristics of the noise being learned by the network and can be used as a preprocessing step to focus on the intensity component of the image, which is often the most affected by noise. As for the self-learning strategy, it plays a crucial role in our approach by allowing the network to adapt to varying noise distributions without requiring explicitly labeled noise-free images. Specifically, our model employs a residual learning framework where the network is trained to predict the noise residual from a given noisy input. This residual is then subtracted from the noisy image to recover a clean image. The training process is guided by a mean squared error loss function that directly measures the discrepancy between the predicted and actual noise components. To be specific, transforming the image to the HSV color space and extracting the V (Value) channel is calculated as follows:  
\[f= \begin{bmatrix}-1&-1&-1\\ -1&8&-1\\ -1&-1&-1\end{bmatrix}  \tag{1}\]  
Another approach to dealing with unknown noise is to train a self-learning strategy denoising algorithm that has generalization ability for different types of noise. This paper proposes a denoising algorithm design and simulation based on image reconstruction. The network structure is shown in Figure 1. The algorithm adopts a twin convolutional network referring to swin-conv (SC) network structure and is trained by constructing a noise original image sample pair. Regarding the twin convolutional network structure, we selected this structure because it enables simultaneous feature extraction from two complementary views of the data--one branch processing the noisy input and the other estimating the corresponding clean representation. This structure helps to enhance the robustness of feature learning by enforcing consistency between the two branches. It integrates two parallel but interdependent convolutional pathways designed to capture complementary features from SAR imagery. Unlike conventional dual-path models, which often process features independently, our SC network employs a feature fusion mechanism that dynamically aggregates spatial and textural information, making it particularly effective for SAR image denoising. The clean noise sample pair of SAR images are shown in Figure 2. The denoising effect can be evaluated by the background suppression factor BSF. The commonly used classic image processing methods for noise reduction include mean filtering, Gaussian filtering, median filtering, bilateral filtering, etc. These classic methods are used to denoise the noise simulation images in the noise simulation dataset, and the denoising effect is compared. Mean filtering is a corresponding operation performed in the spatial domain. During the filtering process, a template is selected, and the pixel value of each point in the image is replaced by the mean of the pixel values of all points in this template. The mean filtering formula is:  
\[g(i,j)=\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}I(i,j) \tag{2}\]  
The denoising effect of the image after applying mean filtering is shown in Figure 3.  
Figure 1: SC network architecture diagram. Figure 3: Image denoising effects of different methods. Figure 2: A clean noise sample pair. Gaussian filtering is a method of blurring an image and removing details and noise. In this sense, it is similar to mean filtering. However, it uses a different weight kernel that represents the shape of a Gaussian (bell shaped) bulge. This method is used for various functions such as image blurring, noise reduction, and detail smoothing.