GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring
GS-RealBlur is a novel data acquisition framework that enables realistic and flexible collection of paired blurry and sharp images for training image deblurring models. It uses a handheld camera for blur shots and a gimbal for dense sharp captures, reconstructs a 3D scene representation, and refines camera poses with a Blur-aware Pose Refinement module. Models trained on the GS-RealBlur dataset outperform those trained on existing synthetic and real-world datasets across multiple benchmarks.
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[Submitted on 16 Jul 2026]
Title:GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring
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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.
Comments: 15 pages
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.15401 [cs.CV]
(or arXiv:2607.15401v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.15401
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mingyang Chen [view email] [v1] Thu, 16 Jul 2026 18:59:01 UTC (19,190 KB)
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