Stable Diffusion Model for Image Restoration to Enhance Low-Quality Satellite Images

Satellite images are important for studying the environment, managing disasters, and analyzing climate change. However, many of these images have low resolution, noise, and distortions, making them hard to interpret accurately. Traditional methods, like interpolation and deep learning-based super-resolution, often fail to recover fine details or keep image quality consistent. This study investigates low-quality satellite image improvement and restoration using Stable Diffusion Model (SDM). This model produces better, more detailed images by progressively eliminating noise in several stages. This research trained the model on a range of satellite images and evaluated its Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID). By maintaining minor details and lowering mistakes, Stable diffusion model is revealed to be better than conventional approaches. These top-notch pictures benefit other remote sensing projects, enhance disaster response planning, and enable more precise research of climate patterns by experts. This study shows that one can effectively improve satellite images using a stable diffusion model.

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