论文标题
实时盲目脱毛，基于轻质深层网络
Real-time Blind Deblurring Based on Lightweight Deep-Wiener-Network
论文作者
论文摘要
在本文中，我们解决了高效率盲目造成盲目的问题。我们建议一组轻巧的深层网络，以实时速度完成任务。该网络包含一个深层神经网络，用于估计Wiener网络的参数和用于DeBlurring的Wiener网络。实验评估表明，就推理时间和参数数量而言，我们的方法在最先进的状态上具有优势。我们的两个型号可以达到每秒100张图像的速度，这有资格用于实时脱张。进一步的研究可能会侧重于我们模型的一些现实世界应用。
In this paper, we address the problem of blind deblurring with high efficiency. We propose a set of lightweight deep-wiener-network to finish the task with real-time speed. The Network contains a deep neural network for estimating parameters of wiener networks and a wiener network for deblurring. Experimental evaluations show that our approaches have an edge on State of the Art in terms of inference times and numbers of parameters. Two of our models can reach a speed of 100 images per second, which is qualified for real-time deblurring. Further research may focus on some real-world applications of deblurring with our models.
