Real-time Blind Deblurring Based on Lightweight Deep-Wiener-Network
This addresses the problem of efficient image deblurring for real-time applications, though it appears incremental as it builds on existing methods with optimizations.
The paper tackles blind deblurring by proposing a lightweight deep-Wiener-network that achieves real-time speed, with models reaching 100 images per second and outperforming state-of-the-art in inference times and parameter counts.
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.