Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring

• URL: http://arxiv.org/abs/2306.00834v1
• Date: Thu, 1 Jun 2023 15:57:12 GMT
• Title: Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring
• Authors: Dan Yang, Mehmet Yamac
• Abstract summary: Event cameras differ from conventional RGB cameras in that they produce asynchronous data sequences. While RGB cameras capture every frame at a fixed rate, event cameras only capture changes in the scene, resulting in sparse and asynchronous data output. Recent state-of-the-art CNN-based deblurring solutions produce multiple 2-D event frames based on the accumulation of event data over a time period. It is particularly useful for scenarios in which exposure times vary depending on factors such as lighting conditions or the presence of fast-moving objects in the scene.
• Score: 7.187030024676791
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event cameras differ from conventional RGB cameras in that they produce asynchronous data sequences. While RGB cameras capture every frame at a fixed rate, event cameras only capture changes in the scene, resulting in sparse and asynchronous data output. Despite the fact that event data carries useful information that can be utilized in motion deblurring of RGB cameras, integrating event and image information remains a challenge. Recent state-of-the-art CNN-based deblurring solutions produce multiple 2-D event frames based on the accumulation of event data over a time period. In most of these techniques, however, the number of event frames is fixed and predefined, which reduces temporal resolution drastically, particularly for scenarios when fast-moving objects are present or when longer exposure times are required. It is also important to note that recent modern cameras (e.g., cameras in mobile phones) dynamically set the exposure time of the image, which presents an additional problem for networks developed for a fixed number of event frames. A Long Short-Term Memory (LSTM)-based event feature extraction module has been developed for addressing these challenges, which enables us to use a dynamically varying number of event frames. Using these modules, we constructed a state-of-the-art deblurring network, Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network (DLEFNet). It is particularly useful for scenarios in which exposure times vary depending on factors such as lighting conditions or the presence of fast-moving objects in the scene. It has been demonstrated through evaluation results that the proposed method can outperform the existing state-of-the-art networks for deblurring task in synthetic and real-world data sets.

Related papers

• CMTA: Cross-Modal Temporal Alignment for Event-guided Video Deblurring [44.30048301161034]
  Video deblurring aims to enhance the quality of restored results in motion-red videos by gathering information from adjacent video frames. We propose two modules: 1) Intra-frame feature enhancement operates within the exposure time of a single blurred frame, and 2) Inter-frame temporal feature alignment gathers valuable long-range temporal information to target frames. We demonstrate that our proposed methods outperform state-of-the-art frame-based and event-based motion deblurring methods through extensive experiments conducted on both synthetic and real-world deblurring datasets.
  arXiv  Detail & Related papers  (2024-08-27T10:09:17Z)
• Temporal-Mapping Photography for Event Cameras [5.838762448259289]
  Event cameras capture brightness changes as a continuous stream of events'' rather than traditional intensity frames. We realize events to dense intensity image conversion using a stationary event camera in static scenes.
  arXiv  Detail & Related papers  (2024-03-11T05:29:46Z)
• EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset [55.12137324648253]
  Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. This paper focuses on five event-aided image and video enhancement tasks.
  arXiv  Detail & Related papers  (2023-12-13T15:42:04Z)
• Event-based Continuous Color Video Decompression from Single Frames [38.59798259847563]
  We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image, using an event camera. Our approach combines continuous long-range motion modeling with a feature-plane-based neural integration model, enabling frame prediction at arbitrary times within the events.
  arXiv  Detail & Related papers  (2023-11-30T18:59:23Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• Revisiting Event-based Video Frame Interpolation [49.27404719898305]
  Dynamic vision sensors or event cameras provide rich complementary information for video frame. estimating optical flow from events is arguably more difficult than from RGB information. We propose a divide-and-conquer strategy in which event-based intermediate frame synthesis happens incrementally in multiple simplified stages.
  arXiv  Detail & Related papers  (2023-07-24T06:51:07Z)
• Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events [63.984927609545856]
  Event-based Inter/intra-frame Compensator (E-IC) is proposed to predict the per-pixel dynamic between arbitrary time intervals. We show that the proposed method achieves state-of-the-art and shows remarkable performance for event-based RS2GS inversion in real-world scenarios.
  arXiv  Detail & Related papers  (2023-04-14T05:30:02Z)
• Event-based Image Deblurring with Dynamic Motion Awareness [10.81953574179206]
  We introduce the first dataset containing pairs of real RGB blur images and related events during the exposure time. Our results show better robustness overall when using events, with improvements in PSNR by up to 1.57dB on synthetic data and 1.08 dB on real event data.
  arXiv  Detail & Related papers  (2022-08-24T09:39:55Z)
• MEFNet: Multi-scale Event Fusion Network for Motion Deblurring [62.60878284671317]
  Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network.
  arXiv  Detail & Related papers  (2021-11-30T23:18:35Z)
• Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction [51.072733683919246]
  We introduce Recurrent Asynchronous Multimodal (RAM) networks to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We show an improvement over state-of-the-art methods by up to 30% in terms of mean depth absolute error.
  arXiv  Detail & Related papers  (2021-02-18T13:24:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.