Removing spatially variant motion blur from a blurry image is a challenging problem as image blur can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single image deblurring can be achieved, but questions about deblurring performance persist. To improve performance, we proposed a deep convolutional neural network to restore a sharp image from a noisy/blurry image pair captured in quick succession. Two neural network structures, Deblur Long Short-Term Memory (LSTM) and DeblurMerger, are presented to fuse the pair of images in either sequential or parallel manner. To boost the training, gradient loss, adversarial loss, and spectral normalization are leveraged. The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO. We evaluated the trained networks on a variety of synthetic datasets and real image pairs. The results demonstrate that the proposed approach outperforms the state-of-the-art methods both qualitatively and quantitatively. DeblurLSTM achieves the best debluring performance, while DeblurMerger achieves nearly the same result but with significantly less computation time.

中文翻译：

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使用嘈杂/模糊图像对去除深度运动模糊

从模糊图像中去除空间变化的运动模糊是一个具有挑战性的问题，因为图像模糊可能很复杂且难以准确建模。深度神经网络的最新进展表明可以实现无内核单图像去模糊，但关于去模糊性能的问题仍然存在。为了提高性能，我们提出了一种深度卷积神经网络，可以从快速连续捕获的嘈杂/模糊图像对中恢复清晰的图像。提出了两种神经网络结构，Deblur Long Short-Term Memory (LSTM) 和 DeblurMerger，以顺序或并行方式融合这对图像。为了促进训练，利用了梯度损失、对抗性损失和谱归一化。由噪声/模糊图像对和相应的地面真实清晰图像组成的训练数据集基于基准数据集 GOPRO 合成。我们在各种合成数据集和真实图像对上评估了经过训练的网络。结果表明，所提出的方法在定性和定量上都优于最先进的方法。DeblurLSTM 实现了最佳去模糊性能，而 DeblurMerger 实现了几乎相同的结果，但计算时间明显减少。