GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring
High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world captured images require complex and inflexible camera systems. In this work, we propose GS-RealBlur, a data acquisition framework for real-world image deblurring, achieving both blur realism and acquisition flexibility. Specifically, we use a handheld camera to capture blurry images, and deploy a gimbal to densely capture sharp images of the same scene. We reconstruct the 3D representation of sharp images and calibrate the camera pose of each blurry frame within this 3D. The image rendered from this 3D according t...
| Authors | Mingyang Chen, Zhilu Zhang, Honglei Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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