Joint Motion Deblurring and Super-Resolution for Single Image Using Diffusion Model and GAN

Blind super-resolution (SR) aims to restore real low-resolution (LR) images. However, most current methods focus on global uniform blur but neglect motion blur, and the few motion deblurring SR methods tend to produce too smooth images. In this letter, we introduce a novel diffusion-based SR method, which can effectively handle the motion blur effect in LR images and retain fine-grained texture information. Our method uses a deblurred feature extraction module and a texture feature extraction module to obtain deblurred features and texture features of the LR image respectively. These two features are then fed into the diffusion model, which samples the image from a learned distribution and outputs a clear and realistic HR image. Moreover, to speed up the sampling process of the diffusion model, we combine it with a conditional generative adversarial network (GAN) to implement stride sampling. Extensive experiments show that our method outperforms state-of-the-art methods in terms of perceptual metrics, and can generate more natural and realistic images.