Removing Blocking Artifacts in Video Streams Using Event Cameras
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Abstract (arXiv)
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors. We first degrade the video frame using a quadtree structure to produce the blocking artifacts to simulate transmitting a video under a heavily constrained bandwidth. Events from the neuromorphic sensor are also simulated, but are transmitted in full. Using the distorted frames and the event stream, EveRestNet is able to improve the image quality.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 12 May 2021.
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12 of 27 references.
References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2019High Speed and High Dynamic Range Video with an Event Cameracited by 344
- 2019Events-to-Video: Bringing Modern Computer Vision to Event Camerascited by 191
- 2017Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarioscited by 150
- 2015Compression Artifacts Reduction by a Deep Convolutional Networkcited by 136
- 2018Event-based High Dynamic Range Image and Very High Frame Rate Video Generation using Conditional Generative Adversarial Networkscited by 97
- 2019EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Cameracited by 65
- 2020EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learningcited by 40
- 2019Learning to Super Resolve Intensity Images from Eventscited by 26
- 2019Event-driven Video Frame Synthesiscited by 8
- 2018Photorealistic Image Reconstruction from Hybrid Intensity and Event based Sensorcited by 4
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