GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring

arXiv:2607.15401v1 Announce Type: new Abstract: 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 blur...