Multi-Bracket High Dynamic Range Imaging with Event Cameras

@article{Messikommer2022MultiBracketHD,
  title={Multi-Bracket High Dynamic Range Imaging with Event Cameras},
  author={Nico Messikommer and Stamatios Georgoulis and Daniel Gehrig and Stepan Tulyakov and Julius Erbach and Alfredo Bochicchio and Yuanyou Li and Davide Scaramuzza},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2022},
  pages={546-556}
}
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times. While these methods work well in static scenes, dynamic scenes remain a challenge since the LDR images still suffer from saturation and noise. In such scenarios, event cameras would be a valid complement, thanks to their higher temporal resolution and dynamic range. In this paper, we propose the first multi-bracket HDR pipeline combining a standard… 
[PDF] Semantic Reader
2 Citations
Background Citations
2

2 Citations

RGB-Event Fusion for Moving Object Detection in Autonomous Driving

RENet is proposed, a novel RGB-Event fusion Network that jointly exploits the two complementary modalities to achieve more robust MOD under challenging scenarios for autonomous driving.

Event-based RGB-D sensing with structured light

This work combines the benefits of ECs and projection-based techniques and allow depth and color detection of static or moving objects with a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, paving the way for frameless RGB-D sensing applications.

References

SHOWING 1-10 OF 69 REFERENCES

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

This paper presents an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet, and proposes to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable alignment module.

EDVR: Video Restoration With Enhanced Deformable Convolutional Networks

This work proposes a novel Video Restoration framework with Enhanced Deformable convolutions, termed EDVR, and proposes a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration.

Deep High Dynamic Range Imaging with Large Foreground Motions

This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions, and produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods.

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021.…

Neuromorphic Camera Guided High Dynamic Range Imaging

  • Jin HanChu Zhou Boxin Shi
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
A hybrid camera system has been built to validate that the proposed method is able to reconstruct quantitatively and qualitatively high-quality high dynamic range images by successfully fusing the images and intensity maps for various real-world scenarios.

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions, and is comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics.

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

  • Lin WangKuk-Jin Yoon
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022
This study hierarchically and structurally group existing deep HDR imaging methods into five categories based on a number/domain of input exposures, number of learning tasks, novel sensor data, novel learning strategies, and applications, and provides a constructive discussion on each category regarding its potential and challenges.

Learning to Reconstruct High Speed and High Dynamic Range Videos from Events

Experimental results verify that the convolutional recurrent neural network presented can generate high speed HDR videos with high quality, and outperform the state-of-the-art reconstruction methods.

How to Calibrate Your Event Camera

It is shown that neural-network–based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras and enables the possibility to perform extrinsical calibration between framebased and event-based sensors without additional complexity.

Robust High Dynamic Range (HDR) Imaging with Complex Motion and Parallax

A Pyramidal Alignment and Masked merging network (PAMnet) that learns to synthesize HDR images from input low dynamic range (LDR) images in an end-to-end manner and can produce ghosting-free HDR results in the presence of large disparity and motion is proposed.
...