Non-Uniform Exposure Imaging via Neuromorphic Shutter Control

• URL: http://arxiv.org/abs/2404.13972v1
• Date: Mon, 22 Apr 2024 08:28:41 GMT
• Title: Non-Uniform Exposure Imaging via Neuromorphic Shutter Control
• Authors: Mingyuan Lin, Jian Liu, Chi Zhang, Zibo Zhao, Chu He, Lei Yu,
• Abstract summary: We propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises. To stabilize the inconsistent Signal-to-Noise Ratio (SNR), we propose an event-based image denoising network within a self-supervised learning paradigm.
• Score: 9.519068512865463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By leveraging the blur-noise trade-off, imaging with non-uniform exposures largely extends the image acquisition flexibility in harsh environments. However, the limitation of conventional cameras in perceiving intra-frame dynamic information prevents existing methods from being implemented in the real-world frame acquisition for real-time adaptive camera shutter control. To address this challenge, we propose a novel Neuromorphic Shutter Control (NSC) system to avoid motion blurs and alleviate instant noises, where the extremely low latency of events is leveraged to monitor the real-time motion and facilitate the scene-adaptive exposure. Furthermore, to stabilize the inconsistent Signal-to-Noise Ratio (SNR) caused by the non-uniform exposure times, we propose an event-based image denoising network within a self-supervised learning paradigm, i.e., SEID, exploring the statistics of image noises and inter-frame motion information of events to obtain artificial supervision signals for high-quality imaging in real-world scenes. To illustrate the effectiveness of the proposed NSC, we implement it in hardware by building a hybrid-camera imaging prototype system, with which we collect a real-world dataset containing well-synchronized frames and events in diverse scenarios with different target scenes and motion patterns. Experiments on the synthetic and real-world datasets demonstrate the superiority of our method over state-of-the-art approaches.

Related papers

• SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams [44.02794438687478]
  Spike cameras have proven effective in capturing motion features and beneficial for solving this ill-posed problem. Existing methods fall into the supervised learning paradigm, which suffers from notable performance degradation when applied to real-world scenarios. We propose the first self-supervised framework for the task of spike-guided motion deblurring.
  arXiv  Detail & Related papers  (2024-03-14T15:29:09Z)
• SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields [14.681688453270523]
  We propose sequential motion understanding radiance fields (SMURF), a novel approach that employs neural ordinary differential equation (Neural-ODE) to model continuous camera motion. Our model, rigorously evaluated against benchmark datasets, demonstrates state-of-the-art performance both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2024-03-12T11:32:57Z)
• Alignment-free HDR Deghosting with Semantics Consistent Transformer [76.91669741684173]
  High dynamic range imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. We propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules.
  arXiv  Detail & Related papers  (2023-05-29T15:03:23Z)
• Realistic Noise Synthesis with Diffusion Models [68.48859665320828]
  Deep image denoising models often rely on large amount of training data for the high quality performance. We propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD) RNSD can incorporate guided multiscale content, such as more realistic noise with spatial correlations can be generated at multiple frequencies.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• 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)
• Recovering Continuous Scene Dynamics from A Single Blurry Image with Events [58.7185835546638]
  An Implicit Video Function (IVF) is learned to represent a single motion blurred image with concurrent events. A dual attention transformer is proposed to efficiently leverage merits from both modalities. The proposed network is trained only with the supervision of ground-truth images of limited referenced timestamps.
  arXiv  Detail & Related papers  (2023-04-05T18:44:17Z)
• Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time [101.91824315554682]
  In this work, we aim ambitiously for a more realistic and challenging task - joint video multi-frame and deblurring under unknown exposure time. We first adopt a variant of supervised contrastive learning to construct an exposure-aware representation from input blurred frames. We then build our video reconstruction network upon the exposure and motion representation by progressive exposure-adaptive convolution and motion refinement.
  arXiv  Detail & Related papers  (2023-03-27T09:43:42Z)
• 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.