Event-Guided Image Super-Resolution Reconstruction

The event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high dynamic range (HDR), high temporal resolution, and low power consumption. However, the large output data caused by the asynchronous imaging mechanism makes the increase in spa...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (4), p.2155
Main Authors: Guo, Guangsha, Feng, Yang, Lv, Hengyi, Zhao, Yuchen, Liu, Hailong, Bi, Guoling
Format: Article
Language:English
Subjects:
Blurring
Cameras
deep learning
event cameras
Frame design
High resolution
image reconstruction
Methods
Neural networks
Sensors
Signal to noise ratio
Spatial resolution
super-resolution
Temporal resolution
Visualization
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Summary:The event camera efficiently detects scene radiance changes and produces an asynchronous event stream with low latency, high dynamic range (HDR), high temporal resolution, and low power consumption. However, the large output data caused by the asynchronous imaging mechanism makes the increase in spatial resolution of the event camera limited. In this paper, we propose a novel event camera super-resolution (SR) network (EFSR-Net) based on a deep learning approach to address the problems of low spatial resolution and poor visualization of event cameras. The network model is capable of reconstructing high-resolution (HR) intensity images using event streams and active sensor pixel (APS) frame information. We design the coupled response blocks (CRB) in the network that are able of fusing the feature information of both data to achieve the recovery of detailed textures in the shadows of real images. We demonstrate that our method is able to reconstruct high-resolution intensity images with more details and less blurring in synthetic and real datasets, respectively. The proposed EFSR-Net can improve the peak signal-to-noise ratio (PSNR) metric by 1-2 dB compared with state-of-the-art methods.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23042155