: Our online deblurring results (bottom) on a number of challenging real-world video frames (top) suffering from strong object motion. Our proposed approach is able to process the input video (VGA) in real-time, i.e. ∼24 fps on a standard graphics card (NVIDIA GTX 1080). State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing methods are based on batch processing and thus need access to all recorded frames, rendering them computationally demanding and time-consuming and thus limiting their practical use. In contrast, we propose an online (sequential) video deblurring method based on a spatio-temporal recurrent network that allows for realtime performance. In particular, we introduce a novel architecture which extends the receptive field while keeping the overall size of the network small to enable fast execution. In doing so, our network is able to remove even large blur caused by strong camera shake and/or fast moving objects. Furthermore, we propose a novel network layer that enforces temporal consistency between consecutive frames by dynamic temporal blending which compares and adap-tively (at test time) shares features obtained at different time steps. We show the superiority of the proposed method in an extensive experimental evaluation.
A spatio-temporal recurrent architecture with a small computational footprint was proposed in REF .
Online Video Deblurring via Dynamic Temporal Blending Network
2017 IEEE International Conference on Computer Vision (ICCV)
2017 IEEE International Conference on Computer Vision (ICCV)
Computer Science