To train a deblurring network, an appropriate dataset with paired blurry and sharp images is essential. Existing datasets collect blurry images either synthetically by aggregating consecutive sharp frames or using sophisticated camera systems to capture real blur. However, these methods offer limited diversity in blur types (blur trajectories) or require extensive human effort to reconstruct large-scale datasets, failing to fully reflect real-world blur scenarios. To address this, we propose GS-Blur, a dataset of synthesized realistic blurry images created using a novel approach. To this end, we first reconstruct 3D scenes from multi-view images using 3D Gaussian Splatting (3DGS), then render blurry images by moving the camera view along the randomly generated motion trajectories. By adopting various camera trajectories in reconstructing our GS-Blur, our dataset contains realistic and diverse types of blur, offering a large-scale dataset that generalizes well to real-world blur. Using GS-Blur with various deblurring methods, we demonstrate its ability to generalize effectively compared to previous synthetic or real blur datasets, showing significant improvements in deblurring performance.

本研究旨在解决传统去模糊数据集中模糊图像多样性不足的问题。通过利用3D高斯散射技术重建3D场景，然后沿随机生成的运动轨迹渲染模糊图像，我们提出了GS-Blur数据集，该数据集能够提供丰富的真实和多样化模糊类型。实验表明，GS-Blur在去模糊性能上相比于以往的数据集有显著提升，能够更好地适应现实模糊场景。

GS-Blur：用于真实图像去模糊的3D场景基础数据集