High-Resolution Dual-Stage Multi-Level Feature Aggregation for Single Image and Video Deblurring

In this paper we address the problem of dynamic scene motion deblurring. We present a model that combines high-resolution processing with a multi-resolution feature aggregation method for single frame and video deblurring. Our proposed model consists of 2 stages. In the first stage, single image deblurring is performed at a very high-resolution. For this purpose, we propose a novel network building block that employs multiple atrous convolutions in parallel. We carefully tune the atrous rate of each of these convolutions to achieve complete coverage of a rectangular area of the input. In this way we obtain a large receptive field at a high spatial resolution. The second stage aggregates information across multiple consecutive frames of a video sequence. Here we maintain a high-resolution, but also use multi-resolution features to mitigate the effects of large movements of objects between images. The presented models rank first and fourth in the NTIRE2020 challenges for single image deblurring and video deblurring, respectively. We apply our framework on current benchmarks and challenges and show that our model provides state-of-the art results.