For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map), is an intricate task for the development of deblurring models. Existing image defocus deblurring methods typically rely on training data collected by specialized imaging equipment, presupposing that these pairs or triplets are perfectly aligned. However, in practical scenarios involving the collection of real-world data, direct acquisition of training triplets is infeasible, and training pairs inevitably encounter spatial misalignment issues. In this work, we introduce a reblurring-guided learning framework for single image defocus deblurring, enabling the learning of a deblurring network even with misaligned training pairs. Specifically, we first propose a baseline defocus deblurring network that utilizes spatially varying defocus blur map as degradation prior to enhance the deblurring performance. Then, to effectively learn the baseline defocus deblurring network with misaligned training pairs, our reblurring module ensures spatial consistency between the deblurred image, the reblurred image and the input blurry image by reconstructing spatially variant isotropic blur kernels. Moreover, the spatially variant blur derived from the reblurring module can serve as pseudo supervision for defocus blur map during training, interestingly transforming training pairs into training triplets. Additionally, we have collected a new dataset specifically for single image defocus deblurring (SDD) with typical misalignments, which not only substantiates our proposed method but also serves as a benchmark for future research.

翻译：对于单图像散焦去模糊任务，获取精确对齐的训练对（或训练三元组）——即一张散焦模糊图像、一张全清晰锐利图像（及一张散焦模糊图）——是开发去模糊模型的一项复杂挑战。现有图像散焦去模糊方法通常依赖专用成像设备采集的训练数据，并默认这些配对或三元组完全对齐。然而，在实际采集真实世界数据的场景中，直接获取训练三元组并不可行，且训练对不可避免地存在空间错位问题。本研究提出一种重模糊引导的单图像散焦去模糊学习框架，使得即使在训练对未对齐的情况下仍能有效学习去模糊网络。具体而言，我们首先提出一个基线散焦去模糊网络，该网络利用空间变化的散焦模糊图作为退化先验以提升去模糊性能。随后，为通过未对齐训练对有效学习该基线网络，我们设计的重模糊模块通过重建空间变化的各向同性模糊核，确保去模糊图像、重模糊图像与输入模糊图像之间的空间一致性。此外，重模糊模块生成的空间变化模糊信息可在训练过程中作为散焦模糊图的伪监督信号，从而将训练对转化为训练三元组。我们还专门收集了一个包含典型错位情况的单图像散焦去模糊数据集（SDD），该数据集不仅验证了所提方法的有效性，也可作为未来研究的基准。