Noise-resistant matching algorithm integrating regional information for low-light stereo vision

Huahui Feng; Geng Zhang; Bingliang Hu; Xin Zhang; Siyuan Li

doi:10.1117/1.JEI.28.1.013050

27 February 2019 Noise-resistant matching algorithm integrating regional information for low-light stereo vision

Huahui Feng, Geng Zhang, Bingliang Hu, Xin Zhang, Siyuan Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013050 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013050

Abstract

Low-light stereo vision is a challenging problem because images captured in dark environment usually suffer from strong random noises. Some widely adopted algorithms, such as semiglobal matching, mainly depend on pixel-level information. The accuracy of local feature matching and disparity propagation decreases when pixels become noisy. Focusing on this problem, we proposed a matching algorithm that utilizes regional information to enhance the robustness to local noisy pixels. This algorithm is based on the framework of ADCensus feature and semiglobal matching. It extends the original algorithm in two ways. First, image segmentation information is added to solve the problem of incomplete path and improve the accuracy of cost calculation. Second, the matching cost volume is calculated with AD-SoftCensus measure that minimizes the impact of noise by changing the pattern of the census descriptor from binary to trinary. The robustness of the proposed algorithm is validated on Middlebury datasets, synthetic data, and real world data captured by a low-light camera in darkness. The results show that the proposed algorithm has better performance and higher matching rate among top-ranked algorithms on low signal-to-noise ratio data and high accuracy on the Middlebury benchmark datasets.

Citation Download Citation

Huahui Feng, Geng Zhang, Bingliang Hu, Xin Zhang, and Siyuan Li "Noise-resistant matching algorithm integrating regional information for low-light stereo vision," Journal of Electronic Imaging 28(1), 013050 (27 February 2019). https://doi.org/10.1117/1.JEI.28.1.013050

Received: 29 August 2018; Accepted: 8 February 2019; Published: 27 February 2019

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