Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While most of the CNN models are generally learned by the reconstruction loss defined on training data, incorporating suitable image priors as well as regularization terms into the network architecture could boost the deblurring performance. In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring. A novel trainable dark and bright channel priors embedded layer (DBCPeL) is developed to aggregate both channel priors and blurry image representations, and a sparse regularization is introduced to regularize the DBCPeNet model learning. Furthermore, we present an effective multi-scale network architecture, namely image full scale exploitation (IFSE), which works in both coarse-to-fine and fine-to-coarse manners for better exploiting information flow across scales. Experimental results on the GoPro and Köhler datasets show that our proposed DBCPeNet performs favorably against state-of-the-art deep image deblurring methods in terms of both quantitative metrics and visual quality.

中文翻译：

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用于动态场景去模糊的暗通道和明通道先验嵌入式网络

近年来，目睹了动态场景去模糊中卷积神经网络（CNN）的重大进步。虽然大多数CNN模型通常是根据训练数据上定义的重建损失来学习的，但将适当的图像先验和正则化项合并到网络体系结构中可以提高去模糊性能。在这项工作中，我们提出了暗通道和明通道先验嵌入式网络（DBCPeNet），以将通道先验插入神经网络中，以有效地进行动态场景去模糊。开发了一种新颖的可训练的暗通道和明通道先验嵌入层（DBCPeL）以聚合通道先验和模糊图像表示，并引入了稀疏正则化来对DBCPeNet模型学习进行正则化。此外，我们提出了一种有效的多尺度网络架构，即图像全尺寸开发（IFSE），它以从粗到精和从细到粗的方式工作，以更好地利用跨尺度的信息流。在GoPro和Köhler数据集上的实验结果表明，我们提出的DBCPeNet在定量指标和视觉质量方面均优于最先进的深度图像去模糊方法。