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

2026-07-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

GS-RealBlur is a novel data acquisition framework designed for real-world image deblurring, addressing the limitations of synthetic data's lack of realism and real-world capture's inflexibility. This framework employs a handheld camera to capture blurry images while simultaneously using a gimbal to densely capture sharp images of the identical scene. It reconstructs a 3D representation from the sharp images and calibrates the camera pose of each blurry frame within this 3D space. A sharp counterpart is then rendered from the 3D representation based on the calibrated pose. To enhance alignment between rendered and blurry images, GS-RealBlur integrates a Blur-aware Pose Refinement (BPR) module, utilizing appearance consistency and centroid alignment constraints. A deblurring model trained on the high-quality, diverse dataset generated by GS-RealBlur demonstrates superior generalization performance across various real-world deblurring benchmarks, consistently surpassing models trained on existing datasets. The code and dataset are slated for public release.

Key takeaway

For Computer Vision Engineers developing image deblurring models, GS-RealBlur presents a critical advancement in data acquisition. If you are struggling with the realism of synthetic datasets or the inflexibility of traditional real-world capture, you should investigate this framework. Adopting its principles, particularly the 3D reconstruction and Blur-aware Pose Refinement, can significantly improve your model's generalization performance on diverse real-world benchmarks, surpassing current synthetic and real-world dataset limitations.

Key insights

GS-RealBlur enables flexible, realistic real-world deblurring data acquisition via 3D reconstruction and blur-aware pose refinement.

Principles

• High-quality paired data is essential for learning-based deblurring.
• 3D scene reconstruction can generate sharp counterparts for blurry images.
• Pose refinement improves alignment between rendered and captured images.

Method

Capture blurry images with handheld camera, sharp images with gimbal. Reconstruct 3D scene, calibrate blurry frame poses, render sharp counterparts, then refine pose using Blur-aware Pose Refinement (BPR).

In practice

• Construct high-quality, diverse datasets for deblurring models.
• Train deblurring models for superior real-world generalization.

Topics

• Image Deblurring
• Data Acquisition Frameworks
• 3D Reconstruction
• Camera Pose Estimation
• Blur-aware Pose Refinement
• Computer Vision Datasets

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.