In this paper, we first develop a principled framework (i.e., Detect&Deblur Netwotks, D2Nets) to leverage non- consecutively blurry frames, and then propose an event fu- sion module (EFM) to bridge the gap between event-driven and video deblurring.

Existing video deblurring methods assume consecutively blurry frames, while neglecting the fact that sharp frames usually appear nearby blurry frame. In this paper, we develop a principled framework D2Nets for video deblurring to exploit non-consecutively blurry frames, and propose a flexible event fusion module ...

In this paper, we develop a principled framework D 2 Nets for video deblurring to exploit non-consecutively blurry frames, and propose a flexible event fusion module (EFM) to bridge the gap between event-driven and video deblurring.

In this paper, we develop a princi- pled framework D2Nets for video deblurring to exploit non- consecutively blurry frames, and propose a flexible event fu- sion module (EFM) to bridge the gap between event-driven and video deblurring. In D2Nets, we propose to first de- tect nearest sharp frames (NSFs) using a ...

Bringing Events into Video Deblurring with Non-consecutively Blurry Frames. Supplementary Material. In this supplementary file, we provide the details of network architecture, more results of ablation study, more results on benchmark datasets and real-world blurry videos. 1. Network Architecture. 1.1. Architectures of ...

Motion Deblurring With Real Events · Code. 2021, ICCV, Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames · Code. 2021, IEEEAccess, Robust Single Image Deblurring Using Gyroscope Sensor. 2022, ECCV, Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance · Code. 2022, ECCV ...

Given the triplet blurring consecutive frames and their corresponding event sequences, SFB is utilized to calculate the spatial consistency between frames and events, and then fuse them properly; TMB restores the long-range dependencies of different event sequences continuously and records them in temporal order, ...

Dec 22, 2023 · Abstract. We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and exploits the spiking representation to parameterize the sharp output video as a mapping from time to intensity.

Mar 7, 2023 · Video deblurring is a computer vision task for obtaining sharp frames by removing the motion blur from blurry frames. A motion blur causes the structure of an object to become blurry and distorts edges in a video frame, thereby lowering the picture quality. A motion blur also ...

A two-stage coarse-refinement network is proposed by adding a frame-based refinement stage that utilizes all the available frames with more abundant textures to further improve the picture quality of the first-stage rough output. Video deblurring aims at removing the motion blur caused by the movement of objects or ...