Denoising of event-based sensors with deep neural networks

Zhihong Zhang; Jinli Suo; Qionghai Dai

doi:10.1117/12.2602742

9 October 2021 Denoising of event-based sensors with deep neural networks

Zhihong Zhang, Jinli Suo, Qionghai Dai

Proceedings Volume 11897, Optoelectronic Imaging and Multimedia Technology VIII; 1189713 (2021) https://doi.org/10.1117/12.2602742
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

As a novel asynchronous imaging sensor, event camera features low power consumption, low temporal latency and high dynamic range, but abundant noise. In real applications, it is essential to suppress the noise in the output event sequences before successive analysis. However, the event camera is of address-event-representation (AER), and requires developing new denoising techniques rather than conventional frame-based image denoising methods. In this paper, we propose two learning-based methods for the denoising of event-based sensor measurements, i.e., convolutional denoising auto-encoder (ConvDAE) and sequence-fragment recurrent neural network (SeqRNN). The former converts the event sequence into 2D images before denoising, which is compatible with existing deep denoisers and high-level vision tasks. The latter, utilizes recurrent neural network’s advantages in dealing with time series to realize online denoising while keeping the event’s original AER representation. Experiments based on real data demonstrate the effectiveness and flexibility of the proposed methods.

Citation Download Citation

Zhihong Zhang, Jinli Suo, and Qionghai Dai "Denoising of event-based sensors with deep neural networks", Proc. SPIE 11897, Optoelectronic Imaging and Multimedia Technology VIII, 1189713 (9 October 2021); https://doi.org/10.1117/12.2602742

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available