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/

翻译：相机抖动（尤其是在大幅或旋转运动下）导致的运动模糊仍然是图像复原领域的主要挑战。本文提出一种深度学习框架，能够从单张模糊图像中联合估计潜在的清晰图像与底层相机运动轨迹。该方法利用投影运动模糊模型（PMBM），通过一个与现代网络兼容的可微分模糊生成模块高效实现。神经网络预测完整的3D旋转轨迹，并以此指导端到端训练的基于模型的复原网络。这种模块化架构通过揭示产生模糊的相机运动，提供了可解释性。此外，该轨迹能够重建生成观测模糊图像的清晰图像序列。为进一步优化结果，我们在推理后通过重模糊损失对轨迹进行优化，从而提升模糊输入与复原输出之间的一致性。大量实验表明，本方法在合成与真实数据集上均取得了最先进的性能，尤其在存在严重或空间变化模糊（端到端去模糊网络难以处理的情况）时表现突出。代码与训练模型发布于 https://github.com/GuillermoCarbajal/Blur2Seq/。