Real-time Blind Deblurring Based on Lightweight Deep-Wiener-Network
Charlie Haywood, Rabih Younes · 2023
In this paper, we address the problem of blind image deblurring with high efficiency. We propose a set of lightweight deep-Wiener-networks to achieve the task with real-time speed. The network contains a deep neural network for estimating the parameters of Wiener networks and a Wiener network for deblurring. Experimental evaluations show that our approaches have an edge on the state-of-the-art in terms of inference speeds and number 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