A Super-Resolution Enhancement Algorithm for Remote Sensing Images Using Conditional Controlled Diffusion Models
Image super-resolution technology enhances image clarity and enriches image detail by improving image spatial resolution,enabling quality enhancement without changing hardware conditions.Given the large size,complex target features,and abundant details of remote sensing images,along with the need for efficient information acquisition,we propose a Diffusion Super-Resolution (DSR) algorithm based on a conditional diffusion model.This approach uses low-resolution remote sensing images from the same region as conditioning inputs to the diffusion model,while high-resolution images with added noise are concatenated as inputs.A deep noise training network was constructed with U-Net as the backbone,incorporating residual connections and self-attention mechanisms.The loss function was also improved for better super-resolution results.The DSR method was tested using high-resolution remote sensing images from multiple periods of the domestic Gaofen and SuperView satellite series.The super-resolution results demonstrated pixel dimension expansion from 32 to 128.Comparative experiments with Bicubic,SRGAN,Real-ESRGAN,and SwinIR super-resolution algorithms showed that the DSR method outperforms these algorithms in both PSNR and SSIM metrics.Additionally,the DSR method significantly improves the quality of multispectral remote sensing images.By leveraging the conditional diffusion model,it successfully preserves rich detail and enhances spatial resolution without compromising image clarity.This method offers an efficient solution for super-resolution reconstruction,ensuring effective information acquisition in remote sensing applications and fulfilling the requirements of various domains such as land use classification,environmental monitoring,and urban planning.Moreover,the DSR method also opens new avenues for future research by demonstrating the potential of diffusion models in remote sensing image processing.It overcomes the limitations of simple convolutional networks,which extract only shallow features,and avoids the convergence issues commonly seen in adversarial neural networks during training,ultimately improving the restoration of rich details in remote sensing images.
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