Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models

• URL: http://arxiv.org/abs/2501.12604v1
• Date: Wed, 22 Jan 2025 03:01:54 GMT
• Title: Image Motion Blur Removal in the Temporal Dimension with Video Diffusion Models
• Authors: Wang Pang, Zhihao Zhan, Xiang Zhu, Yechao Bai,
• Abstract summary: We propose a novel single-image deblurring approach that treats motion blur as a temporal averaging phenomenon.<n>Our core innovation lies in leveraging a pre-trained video diffusion transformer model to capture diverse motion dynamics.<n> Empirical results on synthetic and real-world datasets demonstrate that our method outperforms existing techniques in deblurring complex motion blur scenarios.
• Score: 3.052019331122618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most motion deblurring algorithms rely on spatial-domain convolution models, which struggle with the complex, non-linear blur arising from camera shake and object motion. In contrast, we propose a novel single-image deblurring approach that treats motion blur as a temporal averaging phenomenon. Our core innovation lies in leveraging a pre-trained video diffusion transformer model to capture diverse motion dynamics within a latent space. It sidesteps explicit kernel estimation and effectively accommodates diverse motion patterns. We implement the algorithm within a diffusion-based inverse problem framework. Empirical results on synthetic and real-world datasets demonstrate that our method outperforms existing techniques in deblurring complex motion blur scenarios. This work paves the way for utilizing powerful video diffusion models to address single-image deblurring challenges.

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