State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, School of Information Science and Technology, Northwest University, China.
Xi'an University of Technology, China.
Neural Netw. 2024 Nov;179:106591. doi: 10.1016/j.neunet.2024.106591. Epub 2024 Jul 30.
Most existing model-based and learning-based image deblurring methods usually use synthetic blur-sharp training pairs to remove blur. However, these approaches do not perform well in real-world applications as the blur-sharp training pairs are difficult to be obtained and the blur in real-world scenarios is spatial-variant. In this paper, we propose a self-supervised learning-based image deblurring method that can deal with both uniform and spatial-variant blur distributions. Moreover, our method does not need for blur-sharp pairs for training. In our proposed method, we design the Deblurring Network (D-Net) and the Spatial Degradation Network (SD-Net). Specifically, the D-Net is designed for image deblurring while the SD-Net is used to simulate the spatial-variant degradation. Furthermore, the off-the-shelf pre-trained model is employed as the prior of our model, which facilitates image deblurring. Meanwhile, we design a recursive optimization strategy to accelerate the convergence of the model. Extensive experiments demonstrate that our proposed model achieves favorable performance against existing image deblurring methods.
大多数现有的基于模型和基于学习的图像去模糊方法通常使用合成的模糊-清晰训练对来去除模糊。然而，这些方法在实际应用中表现不佳，因为模糊-清晰训练对很难获得，并且实际场景中的模糊是空间变化的。在本文中，我们提出了一种基于自监督学习的图像去模糊方法，它可以处理均匀和空间变化的模糊分布。此外，我们的方法不需要训练模糊-清晰对。在我们提出的方法中，我们设计了去模糊网络（D-Net）和空间退化网络（SD-Net）。具体来说，D-Net 用于图像去模糊，而 SD-Net 用于模拟空间变化的退化。此外，我们使用现成的预训练模型作为模型的先验，这有助于图像去模糊。同时，我们设计了一种递归优化策略来加速模型的收敛。广泛的实验表明，我们提出的模型在图像去模糊方面取得了良好的性能。
