Generative deep learning models for cloud removal in satellite imagery: A comparative review of GANs and diffusion methods

Satellite imagery provides essential geospatial data to support various remote sensing applications, including environmental monitoring, disaster management, urban planning, and land utilization studies. However, cloud cover often obstructs the clarity and reliability of satellite images, reducing their usefulness. With advances in deep learning, generative models — particularly Generative Adversarial Networks (GANs) and denoising diffusion models — have emerged as promising solutions for cloud removal in satellite imagery. This review systematically evaluates GAN-based and diffusion-based methods, comparing their strengths, limitations, and performance across diverse geographic and cloud conditions. The analysis shows that GANs generate visually realistic outputs through adversarial training, while diffusion models offer superior spatial and structural fidelity due to iterative noise reduction. Integrating auxiliary data such as Synthetic Aperture Radar (SAR) imagery further enhances cloud removal accuracy. This review highlights current challenges and identifies research gaps to support future innovation in satellite image restoration, particularly in cloud removal and generative deep learning for remote sensing.