Blind image deblurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, deblurring networks fail behind the performance of existing deblurring algorithms in case of uniform and 3D blur models. This follows from the diverse and profound effect that the unknown blur-kernel has on the deblurring operator. We propose a new architecture which breaks the deblurring network into an analysis network which estimates the blur, and a synthesis network that uses this kernel to deblur the image. Unlike existing deblurring networks, this design allows us to explicitly incorporate the blur-kernel in the network's training. In addition, we introduce new cross-correlation layers that allow better blur estimations, as well as unique components that allow the estimate blur to control the action of the synthesis deblurring action. Evaluating the new approach over established benchmark datasets shows its ability to achieve state-of-the-art deblurring accuracy on various tests, as well as offer a major speedup in runtime.

中文翻译：

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使用分析综合网络对去模糊

盲图像去模糊仍然是现代人工神经网络的一个具有挑战性的问题。与其他图像恢复问题不同，在均匀和 3D 模糊模型的情况下，去模糊网络的性能落后于现有去模糊算法的性能。这是由于未知模糊核对去模糊算子的不同而深远的影响。我们提出了一种新架构，将去模糊网络分解为估计模糊的分析网络和使用该内核去模糊图像的合成网络。与现有的去模糊网络不同，这种设计允许我们在网络训练中明确地结合模糊内核。此外，我们引入了新的互相关层，允许更好的模糊估计，以及允许估计模糊控制合成去模糊动作的独特组件。在已建立的基准数据集上评估新方法表明它能够在各种测试中实现最先进的去模糊精度，并在运行时提供重大加速。