Noise removing method for low-light-level image based on four-direction nonlocal means

Xiangyu Kong; Yunsheng Qian; Gangcheng Jiao; Zhuang Miao

doi:10.1117/12.2559777

17 April 2020 Noise removing method for low-light-level image based on four-direction nonlocal means

Xiangyu Kong, Yunsheng Qian, Gangcheng Jiao, Zhuang Miao

Proceedings Volume 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications; 114550T (2020) https://doi.org/10.1117/12.2559777
Event: Sixth Symposium on Novel Photoelectronic Detection Technology and Application, 2019, Beijing, China

Abstract

Low-light-level (LLL) images, which are captured in the extreme environment without enough light for human eyes. LLL images quality degenerates badly with light decrease. The heavy noises and low contrast of the LLL images impede the access to the targets information. In order to enhance LLL images, we propose a two-step method to remove the complex noises. First, median filtering is adopted to remove the salt and pepper noises which are often introduced from the bad pixels. Second, the four-direction nonlocal means algorithm is adopted. The experimental results show that the proposed method can remove the noises and keep the details of the low illumination level scene effectively.

Citation Download Citation

Xiangyu Kong, Yunsheng Qian, Gangcheng Jiao, and Zhuang Miao "Noise removing method for low-light-level image based on four-direction nonlocal means", Proc. SPIE 11455, Sixth Symposium on Novel Optoelectronic Detection Technology and Applications, 114550T (17 April 2020); https://doi.org/10.1117/12.2559777

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