Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames

• URL: http://arxiv.org/abs/2306.15507v1
• Date: Tue, 27 Jun 2023 14:30:25 GMT
• Title: Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames
• Authors: Yunfan Lu, Guoqiang Liang, Lin Wang
• Abstract summary: This paper makes the first attempt to tackle the challenging task of recovering arbitrary frame rate latent global shutter (GS) frames from two consecutive rolling shutter (RS) frames. We propose a novel self-supervised framework that leverages events to guide RS frame correction VFI in a unified framework.
• Score: 6.62974666987451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper makes the first attempt to tackle the challenging task of recovering arbitrary frame rate latent global shutter (GS) frames from two consecutive rolling shutter (RS) frames, guided by the novel event camera data. Although events possess high temporal resolution, beneficial for video frame interpolation (VFI), a hurdle in tackling this task is the lack of paired GS frames. Another challenge is that RS frames are susceptible to distortion when capturing moving objects. To this end, we propose a novel self-supervised framework that leverages events to guide RS frame correction and VFI in a unified framework. Our key idea is to estimate the displacement field (DF) non-linear dense 3D spatiotemporal information of all pixels during the exposure time, allowing for the reciprocal reconstruction between RS and GS frames as well as arbitrary frame rate VFI. Specifically, the displacement field estimation (DFE) module is proposed to estimate the spatiotemporal motion from events to correct the RS distortion and interpolate the GS frames in one step. We then combine the input RS frames and DF to learn a mapping for RS-to-GS frame interpolation. However, as the mapping is highly under-constrained, we couple it with an inverse mapping (i.e., GS-to-RS) and RS frame warping (i.e., RS-to-RS) for self-supervision. As there is a lack of labeled datasets for evaluation, we generate two synthetic datasets and collect a real-world dataset to train and test our method. Experimental results show that our method yields comparable or better performance with prior supervised methods.

Related papers

• UniINR: Event-guided Unified Rolling Shutter Correction, Deblurring, and Interpolation [20.866360240444426]
  Video frames captured by rolling shutter (RS) cameras during fast camera movement frequently exhibit RS distortion and blur simultaneously. We propose the first and novel approach, named UniINR, to recover arbitrary frame-rate sharp GS frames from an RS blur frame and paired events. Our method features a lightweight model with only 0.38M parameters, and it also enjoys high efficiency, achieving 2.83ms/frame in 31 times frame-rate of an RS blur frame.
  arXiv  Detail & Related papers  (2023-05-24T11:57:03Z)
• 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)
• Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video [111.08121952640766]
  This paper presents a novel deep-learning based solution to the RS temporal super-resolution problem. By leveraging the multi-view geometry relationship of the RS imaging process, our framework successfully achieves high framerate GS generation. Our method can produce high-quality GS image sequences with rich details, outperforming the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-10-06T16:47:12Z)
• Towards Frame Rate Agnostic Multi-Object Tracking [76.82407173177138]
  We propose a Frame Rate Agnostic MOT framework with a Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information. FAPS reflects all post-processing steps in training via tracking pattern matching and fusion.
  arXiv  Detail & Related papers  (2022-09-23T04:25:19Z)
• Context-Aware Video Reconstruction for Rolling Shutter Cameras [52.28710992548282]
  In this paper, we propose a context-aware GS video reconstruction architecture. We first estimate the bilateral motion field so that the pixels of the two RS frames are warped to a common GS frame. Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames.
  arXiv  Detail & Related papers  (2022-05-25T17:05:47Z)
• Bringing Rolling Shutter Images Alive with Dual Reversed Distortion [75.78003680510193]
  Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time. We develop a novel end-to-end model, IFED, to generate dual optical flow sequence through iterative learning of the velocity field during the RS time.
  arXiv  Detail & Related papers  (2022-03-12T14:57:49Z)
• TimeLens: Event-based Video Frame Interpolation [54.28139783383213]
  We introduce Time Lens, a novel indicates equal contribution method that leverages the advantages of both synthesis-based and flow-based approaches. We show an up to 5.21 dB improvement in terms of PSNR over state-of-the-art frame-based and event-based methods.
  arXiv  Detail & Related papers  (2021-06-14T10:33:47Z)

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.