Urban Remote Sensing Image Super Resolution based on Denoising Diffusion Probabilistic Models and Generative Adversarial Networks

The need for high-resolution remote sensing images is growing rapidly, driven by applications in urban planning, environmental monitoring, and resource management. This study introduces a novel framework for urban remote sensing image super-resolution by integrating Denoising Diffusion Probabilistic Models (DDPM) with Generative Adversarial Networks (GANs). The hybrid model leverages DDPM's robust denoising and feature reconstruction capabilities alongside GANs' ability to enhance perceptual fidelity and spatial detail. Evaluated on benchmark datasets (NWPU-RESISC45, UCMerced Land Use, and Sfax aerial imagery), the model achieves a PSNR of 31.60 dB, SSIM of 0.81, and LPIPS of 0.31, outperforming existing state-of-the-art methods. With an inference time of 2.70 seconds per image, the framework is computationally efficient and well-suited for large-scale urban applications.