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Published on: September 5, 2019
Long&short Exposures Guided Diffusion Model for Realistic Local Motion Deblurring
Summary
This study introduces ExpDiff, a new method for local motion deblurring using a diffusion model. It effectively removes blur from moving objects in low-light conditions, improving image quality.
Area of Science:
- Computer Vision
- Image Processing
- Artificial Intelligence
Background:
- Local motion deblurring is challenging, particularly in low signal-to-noise ratio (SNR) environments.
- Existing methods struggle with inaccurate blur detection and unsatisfactory results when focusing only on blurred areas.
Purpose of the Study:
- To develop an effective diffusion model for realistic local motion deblurring.
- To improve blur detection accuracy and image restoration quality.
Main Methods:
- A context-based local blur detection module leveraging contextual information for semantically coherent blur region identification.
- A blurry-aware guided image restoration method that discriminately handles blurry and clear regions.
- A structure-guided diffusion model utilizing long-exposure and short-exposure images for realistic restoration.
Main Results:
- The proposed ExpDiff method achieves realistic local motion deblurring.
- Experimental results demonstrate favorable performance compared to state-of-the-art methods.
- The method effectively preserves the integrity of blur regions through context-based detection.
Conclusions:
- ExpDiff offers a robust solution for local motion deblurring, especially in challenging low-SNR conditions.
- The integration of context-based blur detection and a diffusion model enhances restoration realism.
- The end-to-end trained ExpDiff method shows significant improvements over existing techniques.
