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

2026-07-16

AI Summary

Existing image deblurring methods struggle to balance realism and flexibility due to the limited photorealism of synthetic data or the complexity of real-world data acquisition. This work proposes a flexible and efficient framework for capturing realistic deblurring data by using handheld cameras to acquire blurry images, while a gimbal-mounted camera densely captures sharp images to reconstruct the 3D scene. Paired sharp images are then rendered using estimated camera poses. A key innovation is the introduction of a Blur-aware Pose Refinement (BPR) module, which significantly improves geometric alignment between blurry and sharp image pairs. Leveraging this framework, the authors construct RealDeblur—a high-quality, diverse dataset—and demonstrate that models trained on it substantially outperform state-of-the-art methods across multiple real-world benchmarks, exhibiting strong generalization capability.

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 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 to the pose serves as the sharp counterpart. To better align the rendered image with the blurry image, we introduce a Blur-aware Pose Refinement (BPR) module that refines the pose using appearance consistency and centroid alignment constraints. Leveraging GS-RealBlur, we construct a high-quality and diverse dataset. Extensive experiments demonstrate that a deblurring model trained on our dataset achieves superior generalization performance across various real-world deblurring benchmarks, consistently outperforming models trained on existing synthetic and real-world datasets. The code and dataset will be made publicly available.

Problem

• image deblurring
• real-world blur
• paired data
• data acquisition
• blur realism

Innovation

• GS-RealBlur
• real-world deblurring
• 3D scene reconstruction
• pose refinement
• paired dataset