论文标题
使用分析合成网络对脱毛
Deblurring using Analysis-Synthesis Networks Pair
论文作者
论文摘要
对于现代人工神经网络而言，盲目的图像浮雕仍然是一个具有挑战性的问题。与其他图像恢复问题不同，在统一和3D Blur模型的情况下，DeBlurring网络在现有Deblurring算法的性能之后失败。这取决于未知的模糊内核对脱毛操作员的多种多样和深刻的影响。 我们提出了一种新的体系结构，该架构将Deblurring网络打破到一个分析网络中，该网络估计模糊，以及一个使用此内核来删除图像的合成网络。与现有的DeBlurring网络不同，这种设计使我们能够将模糊内核的明确纳入网络培训中。此外，我们引入了新的互相关层，以允许更好的模糊估计以及允许估计模糊控制合成脱毛作用的作用的独特组件。 评估已建立的基准数据集的新方法表明其在各种测试中实现最先进的脱脂精度的能力，并在运行时提供了主要的加速。
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.
