论文标题
神经最大估计不成对运动的后验估计
Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring
论文作者
论文摘要
真实世界的动态场景长期以来一直是一项具有挑战性的任务，因为配对模糊的训练数据是不可用的。常规的最大后验估计和基于深度学习的脱蓝色方法分别受到手工制作的先验和合成模糊的训练对限制，因此未能推广到真正的动态模糊性。为此，我们提出了一个神经最大的后验（神经MAP）估计框架，用于训练神经网络，以从未配对的数据中恢复盲运动信息和清晰的内容。所提出的Nerumap由运动估计网络和一个脱毛网络组成，该网络经过训练，以建模（重新）模糊过程（即可能性函数）。同时，对运动估计网络进行了训练，可以通过应用隐式动态运动来探索图像中的运动信息，并作为回报执行Deblurring网络训练（即提供尖锐的图像之前）。所提出的神经模式是现有DEBLURING神经网络的正交方法，并且是第一个框架，它使培训图像deblurring网络在未配对的数据集中进行培训。实验证明了我们对定量指标和视觉质量的优势，而不是最先进的方法。代码可在https://github.com/yjzhang96/neurmap-deblur上找到。
Real-world dynamic scene deblurring has long been a challenging task since paired blurry-sharp training data is unavailable. Conventional Maximum A Posteriori estimation and deep learning-based deblurring methods are restricted by handcrafted priors and synthetic blurry-sharp training pairs respectively, thereby failing to generalize to real dynamic blurriness. To this end, we propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeruMAP consists of a motion estimation network and a deblurring network which are trained jointly to model the (re)blurring process (i.e. likelihood function). Meanwhile, the motion estimation network is trained to explore the motion information in images by applying implicit dynamic motion prior, and in return enforces the deblurring network training (i.e. providing sharp image prior). The proposed NeurMAP is an orthogonal approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets. Experiments demonstrate our superiority on both quantitative metrics and visual quality over state-of-the-art methods. Codes are available on https://github.com/yjzhang96/NeurMAP-deblur.
