Research on Speckle Filtering Method for Synthetic Aperture Radar Images Based on Deep Learning

In order to optimize the clarity of synthetic aperture radar (SAR) images and enhance the accuracy of target recognition, this paper proposes a SAR image denoising method using deep learning techniques. This method utilizes Retinex theory to estimate and eliminate lighting components through Gaussian kernel function, supplemented by logarithmic transformation and multi-scale weight superposition techniques, thereby improving the contrast of SAR images and preserving their texture features. Furthermore, the processed SAR image is subjected to multi-level decomposition using Laplace pyramid technique, and the details and noise in the image are separated using EXPAND operation and bandpass filtering technology to achieve better noise elimination effect. In addition, this article also introduces Generative Adversarial Networks (GANs), which are trained through the game between the generator and discriminator to understand the complex distribution characteristics of SAR images and achieve adaptive denoising processing. The experimental results show that the proposed method exhibits higher signal-to-noise ratio and lower image entropy in all tests, indicating its excellent performance in noise reduction and information preservation.