Long&short Exposures Guided Diffusion Model for Realistic Local Motion Deblurring

Removing local blur caused by moving objects is challenging, especially in low signal-to-noise ratio environments, as the moving objects are usually significantly blurry while the static background remains relatively clear. Existing methods relying on local blur detection often suffer from inaccuracies and cannot generate satisfactory results when focusing solely on blurred regions. In this paper, we present an effective diffusion model guided with long-exposure and short-exposure images for realistic local motion deblurring. Specifically, we first propose a context-based local blur detection module. Different from existing methods that rely on pixel-wise classification, we leverage contextual information to generate semantically coherent blur region identification, which preserves the integrity of blur regions. Then, we present a blurry-aware guided image restoration method to remove local blur by discriminately handling the blurry and clear regions. Finally, a structure-guided diffusion model is developed to achieve realistic image restoration by exploring useful information from the above blurry-aware guided image restoration result and the short-exposure image as guidance. The proposed method, ExpDiff, is trained in an end-to-end manner. Our extensive experimental results show that the proposed ExpDiff performs favorably against state-of-the-art methods.