A Noise-Robust Blind Deblurring Algorithm With Wavelet-Enhanced Diffusion Model for Optical Remote Sensing Images

Blind deblurring of optical remote sensing images has been a longstanding challenge. In recent years, many learning-based deblurring algorithms have been greatly developed. However, these methods often suffer from losing image texture details and some artificial artifacts under low signal-to-noise ratio (SNR) conditions. To tackle this challenge, we introduce an innovative end-to-end noise-robust blind deblurring algorithm based on the diffusion model joined with a denoising module and a wavelet-enhanced conditional embedding mechanism. Experiments have verified the effectiveness of our method. Compared to the image blind deblurring algorithms based on the diffusion model, the proposed algorithm demonstrates better performance in terms of quantitative metrics peak signal-to-noise ratio and structural similarity index. Compared to all the comparative algorithms, the proposed algorithm exhibits significant advantages in quantitative metrics learned perceptual image patch similarity, Fréchet inception distance, and natural image quality evaluator and shows superior visual effects in restoring texture details in the restored images, especially in challenging low SNR conditions.