A Deep Learning-Based RIDNet Approach for Enhanced Denoising of SAR Images

Synthetic Aperture Radar (SAR) imagery is crucial in remote sensing applications, such as military surveillance, environmental monitoring, and disaster response. However, speckle noise often degraded SAR images, compromising image clarity and structural integrity. In this study, we present a deep learning-driven custom Real Image Denoising Network (RIDNet) framework designed to effectively de-noise high-resolution SAR imagery. Our approach integrates advanced clutter normalization techniques with attention mechanisms and Swift Transformer layers to address unique noise patterns in SAR data while preserving fine image details. Leveraging data augmentation and enhanced generative algorithms, the custom RIDNet model achieves notable improvements in image quality, as measured by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores. Comparative analysis with existing denoising models demonstrates the superiority of our framework in reducing noise and maintaining structural fidelity across diverse SAR imaging conditions. This work provides a robust, scalable solution for SAR image denoising, supporting enhanced visual clarity and improved interpretability for critical applications in high-stakes environments.