AI 中文总结
针对训练图像去模糊模型高质量配对数据难获取的问题，提出GS-RealBlur框架，通过手持相机与万向节配合采集数据，经3D重建、姿态校准及BPR模块优化，构建数据集，所训模型在真实世界去模糊基准测试中泛化性能卓越。
AI 中文摘要
高质量、大规模的配对数据对于训练基于学习的图像去模糊模型至关重要。合成模糊图像缺乏真实感，而真实世界捕获图像需要复杂且不灵活的相机系统。本文提出GS-RealBlur，一种用于真实世界图像去模糊的数据采集框架，兼具模糊真实感和采集灵活性。具体使用手持相机捕获模糊图像，用万向节密集捕获同一场景的清晰图像，重建清晰图像的3D表示并校准模糊帧相机姿态，根据姿态渲染的图像作为清晰对应图像。为更好对齐，引入模糊感知姿态优化（BPR）模块。利用该框架构建了高质量多样数据集。实验表明，在该数据集上训练的去模糊模型在各种真实世界去模糊基准测试中具有卓越泛化性能，优于在现有合成和真实世界数据集上训练的模型。代码和数据集将公开。
英文摘要
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.
Comments15 pages
