# A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
###### Abstract  
Due to its high resolution and all-weather imaging capability, Synthetic Aperture Radar (SAR) is widely used in fields such as Earth observation and environmental monitoring. However, SAR images are prone to noise interference during the imaging process, which seriously affects the visualization effect and subsequent analysis of the image. This article proposes a convolutional neural network SAR image denoising algorithm based on self-learning strategy. Particularly, a denoising convolutional neural network that utilizes a self-learning denoising model and a twin convolutional network structure is proposed. By constructing a noise original image dataset sample pair for training, the model can automatically learn image features and noise distribution, significantly improving the denoising effect of SAR images and possessing stronger generalization ability. The simulation experiments verified the effectiveness of the proposed method, indicating its potential application in SAR image denoising. s +
Footnote †: journal: Applied sciences  
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images [10; 11]. In addition, model-based methods such as wavelet transform and statistical model denoising have also been widely applied in SAR image denoising [12; 13]. However, these methods often rely on accurate modeling of the statistical characteristics of noise, and their performance is not ideal in complex scenarios. Despeckling methods, including spatial filters, transform-domain techniques, and non-local means (NLM), often struggle with over-smoothing or introducing artifacts. The aforementioned methods generally rely on predefined models and assumptions that may not effectively capture the complex, nonlinear nature of SAR data, potentially limiting their denoising performance. In contrast, deep learning-based methods can learn intricate patterns and representations directly from data, enabling them to adapt to the unique characteristics of SAR imagery and achieve superior noise reduction. Particularly, Convolutional Neural Networks (CNNs) [14; 15; 16], have demonstrated excellent performance by learning complex noise-to-clean mappings. However, their reliance on paired \"noisy-clean\" datasets for supervised training introduces a critical limitation. Real-world SAR applications often lack noise-free ground truth images, forcing models to rely on synthetic data with domain gaps in terms of scattering and noise characteristics. This mismatch results in suboptimal performance when applied to real SAR images. Self-supervised learning strategies, have recently gained attention by eliminating the need for clean references. For instance, [PERSON] et al. [17] proposed a segmentation-guided semantic-aware self-supervised SAR image denoising method, named SARDeSeg. This method combines a segmentation network with a denoising network, guiding it to learn and perceive the semantic information of the noisy input SAR images. The self-supervised enhanced Noise2 Noise (EN2N) method is proposed in [18], which addresses the challenges of spatial detail loss, dependence on noise-free training data, and low computational efficiency of traditional methods in removing multiplicative noise from SAR images [19]. This is achieved by integrating pre-trained CNN features with a hybrid loss function. In recent years, the rapid development of deep learning technology has provided new solutions for SAR image denoising. Deep learning algorithms can automatically learn features from data by constructing complex neural network models, thereby achieving better performance in denoising tasks. For example, the successful application of CNNs in image processing has inspired researchers to apply it to denoising SAR images [20; 21]. For example, reference [22] proposes combining residual learning and batch normalization strategies to propose the SAR-CNN denoising algorithm, which can achieve some improvements in running speed and denoising performance.