从一张模糊图同时恢复清晰图像和相机运动轨迹。
Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image
- 基于可微模糊模块，联合建模相机运动与图像复原。
- 在严重模糊和空间变模糊场景下优于现有方法。
- 适合需要运动轨迹重建的视觉任务研究者。
由相机抖动引起的运动模糊，尤其在大幅或旋转运动下，仍是图像复原的重大挑战。本文提出一种深度学习框架，仅凭单张模糊图像即可联合估计潜在清晰图像与相机运动轨迹。方法基于可微的投影运动模糊模型（PMBM），通过可微模糊生成模块实现高效计算，兼容现代神经网络结构。神经网络预测完整的3D旋转轨迹，指导基于模型的复原网络端到端训练。该模块化架构具备可解释性，能揭示导致模糊的相机运动，并进一步重建生成模糊图像的清晰图像序列。为提升结果质量，引入重模糊损失，在推理后优化轨迹，增强输入模糊图与输出复原图的一致性。大量实验表明，本方法在合成与真实数据集上均达到领先性能，尤其在严重或空间变异性模糊场景中表现优异，传统端到端去模糊网络在此类情况下难以应对。代码与训练模型已开源。
原文摘要 · Abstract (English)
Motion blur caused by camera shake, particularly under large or rotational movements, remains a major challenge in image restoration. We propose a deep learning framework that jointly estimates the latent sharp image and the underlying camera motion trajectory from a single blurry image. Our method leverages the Projective Motion Blur Model (PMBM), implemented efficiently using a differentiable blur creation module compatible with modern networks. A neural network predicts a full 3D rotation trajectory, which guides a model-based restoration network trained end-to-end. This modular architecture provides interpretability by revealing the camera motion that produced the blur. Moreover, this trajectory enables the reconstruction of the sequence of sharp images that generated the observed blurry image. To further refine results, we optimize the trajectory post-inference via a reblur loss, improving consistency between the blurry input and the restored output. Extensive experiments show that our method achieves state-of-the-art performance on both synthetic and real datasets, particularly in cases with severe or spatially variant blur, where end-to-end deblurring networks struggle. Code and trained models are available at https://github.com/GuillermoCarbajal/Blur2Seq/
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