Synthetic Aperture Radar (SAR) technology stands at the forefront of capturing and processing Earth’s surface visuals due to its widespread acceptance across various organizations. However, the presence of unwanted random granular interference, commonly referred to as “speckle," poses a significant challenge in SAR data processing. Addressing this challenge, known as “despeckling," is crucial for extracting clear SAR visuals. This article introduces a novel CNN-based approach for despeckling SAR visuals contaminated with speckle. Our proposed model integrates a Generative Adversarial Network (GAN) module to estimate the distribution of contaminating speckle components from the input SAR data. Concurrently, a gradient estimator module captures the crisp changes in textural information within the input data. Subsequently, the input SAR data, the estimated noise distribution, and the extracted gradient undergo further processing through a deep convolutional module to generate a clean SAR visual. Unlike traditional methods that focus solely on learning the residual noisy component or the clean data, our proposed despeckling model learns the degradation pattern caused by noisy components while emphasizing gradient information, thereby capturing critical minute information. Experimental results demonstrate that our methodology significantly enhances despeckling performance compared to existing technologies in the literature. This research presents a promising step forward in advancing SAR visual despeckling techniques, with implications for improved data quality and interpretation in various applications.