Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation

• URL: http://arxiv.org/abs/2403.09392v1
• Date: Thu, 14 Mar 2024 13:45:09 GMT
• Title: Event-based Asynchronous HDR Imaging by Temporal Incident Light Modulation
• Abstract summary: We propose a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging. Our proposed Asyn system integrates the Dynamic Vision Sensors (DVS) with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams.
• Score: 54.64335350932855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic Range (DR) is a pivotal characteristic of imaging systems. Current frame-based cameras struggle to achieve high dynamic range imaging due to the conflict between globally uniform exposure and spatially variant scene illumination. In this paper, we propose AsynHDR, a Pixel-Asynchronous HDR imaging system, based on key insights into the challenges in HDR imaging and the unique event-generating mechanism of Dynamic Vision Sensors (DVS). Our proposed AsynHDR system integrates the DVS with a set of LCD panels. The LCD panels modulate the irradiance incident upon the DVS by altering their transparency, thereby triggering the pixel-independent event streams. The HDR image is subsequently decoded from the event streams through our temporal-weighted algorithm. Experiments under standard test platform and several challenging scenes have verified the feasibility of the system in HDR imaging task.

Related papers

• High-Speed Full-Color HDR Imaging via Unwrapping Modulo-Encoded Spike Streams [83.83721273785281]
  We present a complete modulo-based HDR imaging system that enables high-speed, full-color HDR acquisition.<n>At the core of our approach is an exposure-decoupled formulation of modulo imaging that allows multiple measurements to be interleaved in time.<n>To validate the practical viability of our system, we demonstrate a proof-of-concept hardware implementation based on modulo-encoded spike streams.
  arXiv  Detail & Related papers  (2026-04-16T05:20:38Z)
• InstantHDR: Single-forward Gaussian Splatting for High Dynamic Range 3D Reconstruction [48.64335106961342]
  Existing HDR pipelines rely on known camera poses, well-calibrated dense point clouds, and time-consuming per-scene optimization.<n>Current feed-forward alternatives overlook the HDR problem by assuming exposure-in appearance.<n>We propose Instant, a feed-forward network that reconstructs 3D HDR scenes from uncalibrated multi-exposure LDR collections in a single forward pass.
  arXiv  Detail & Related papers  (2026-03-11T20:51:47Z)
• HDR-NSFF: High Dynamic Range Neural Scene Flow Fields [25.023351117641358]
  We present HDR-NSFF, a paradigm shift from 2D-based merging to 4D-temporal modeling.<n>Experiments demonstrate that HDR-NSFF recovers fine details radiance coherent dynamics even under challenging exposure variations.<n>We present the first real-world HDR-GoPro dataset specifically designed for dynamic HDR scenes.
  arXiv  Detail & Related papers  (2026-03-09T12:36:46Z)
• Dynamic Novel View Synthesis in High Dynamic Range [78.72910306733607]
  Current methods primarily focus on static scenes, implicitly assuming all scene elements remain stationary and non-living.<n>We introduce HDR-4DGS, a Gaussian Splatting-based architecture featured with an innovative dynamic tone-mapping module.<n>Experiments demonstrate that HDR-4DGS surpasses existing state-of-the-art methods in both quantitative performance and visual fidelity.
  arXiv  Detail & Related papers  (2025-09-26T04:29:22Z)
• Event-assisted 12-stop HDR Imaging of Dynamic Scene [20.064191181938533]
  We propose a novel 12-stop HDR imaging approach for dynamic scenes, leveraging a dual-camera system with an event camera and an RGB camera. The event camera provides temporally dense, high dynamic range signals that improve alignment between LDR frames with large exposure differences, reducing ghosting artifacts caused by motion. Our method achieves state-of-the-art performance, successfully extending HDR imaging to 12 stops in dynamic scenes.
  arXiv  Detail & Related papers  (2024-12-19T10:17:50Z)
• Exposure Completing for Temporally Consistent Neural High Dynamic Range Video Rendering [17.430726543786943]
  We propose a novel paradigm to render HDR frames via completing the absent exposure information. Our approach involves interpolating neighbor LDR frames in the time dimension to reconstruct LDR frames for the absent exposures. This benefits the fusing process for HDR results, reducing noise and ghosting artifacts therefore improving temporal consistency.
  arXiv  Detail & Related papers  (2024-07-18T09:13:08Z)
• Generating Content for HDR Deghosting from Frequency View [56.103761824603644]
  Recent Diffusion Models (DMs) have been introduced in HDR imaging field. DMs require extensive iterations with large models to estimate entire images. We propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging.
  arXiv  Detail & Related papers  (2024-04-01T01:32:11Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes [58.66427721308464]
  Self is a self-supervised reconstruction method that only requires dynamic multi-exposure images during training. Self achieves superior results against the state-of-the-art self-supervised methods, and comparable performance to supervised ones.
  arXiv  Detail & Related papers  (2023-10-03T07:10:49Z)
• LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction [20.911738532410766]
  We propose an end-to-end HDR video composition framework, which aligns LDR frames in feature space and then merges aligned features into an HDR frame. In training, we adopt a temporal loss, in addition to frame reconstruction losses, to enhance temporal consistency and thus reduce flickering.
  arXiv  Detail & Related papers  (2023-08-22T01:43:00Z)
• 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)
• SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders [97.64072440883392]
  We propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Unlikely previous methods, directly recovering content and removing ghosts simultaneously, which is hard to achieve optimum. Experiments demonstrate that SSHDR outperforms state-of-the-art methods quantitatively and qualitatively within and across different datasets.
  arXiv  Detail & Related papers  (2023-04-14T03:42:51Z)
• SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes [17.867412310873732]
  Ghosting artifacts, motion blur, and low fidelity in highlight are the main challenges in High Dynamic Range (LDR) imaging. We propose a joint HDR and denoising pipeline, containing two sub-networks. We create the first joint HDR and denoising benchmark dataset.
  arXiv  Detail & Related papers  (2022-06-20T07:49:56Z)
• HDR Reconstruction from Bracketed Exposures and Events [12.565039752529797]
  Reconstruction of high-quality HDR images is at the core of modern computational photography. We present a multi-modal end-to-end learning-based HDR imaging system that fuses bracketed images and event in the feature domain. Our framework exploits the higher temporal resolution of events by sub-sampling the input event streams using a sliding window.
  arXiv  Detail & Related papers  (2022-03-28T15:04:41Z)
• Self-supervised HDR Imaging from Motion and Exposure Cues [14.57046548797279]
  We propose a novel self-supervised approach for learnable HDR estimation that alleviates the need for HDR ground-truth labels. Experimental results show that the HDR models trained using our proposed self-supervision approach achieve performance competitive with those trained under full supervision.
  arXiv  Detail & Related papers  (2022-03-23T10:22:03Z)

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.