Abstract

The filtering parameters of non-local means (NLM) that seriously affect the denoising results are usually optimized based on the noise level estimation. But the noise level is unknown in many cases. In this paper, a simple method for optimizing NLM parameters based on autocorrelation function (ACF) is proposed from the perspective of signal estimation, which uses the periodic lag of the autocorrelation function to estimate the vibration signal. This method is verified with a hypothetical clean image corrupted by additive noise, and the NLM parameter optimization results are compared with those obtained by two traditional methods based on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) respectively. Then it is tested with the practical vibration signal of a phase-sensitive optical time-domain reflectometer ( $\varphi$ -OTDR). The fidelity and spatial resolution of the denoised practical signal are analyzed. This work helps to improve the performance of $\varphi$ -OTDR and signal recognition.

Nonlocal means image denoising using orthogonal moments

Ahlad Kumar
Appl. Opt. 54(27) 8156-8165 (2015)

Machine learning methods for identification and classification of events in ϕ-OTDR systems: a review

Deus F. Kandamali, Xiaomin Cao, Manling Tian, Zhiyan Jin, Hui Dong, and Kuanglu Yu
Appl. Opt. 61(11) 2975-2997 (2022)

Estimate optimization parameters for incoherent backscatter heterodyne lidar including unknown return signal bandwidth

Barry J. Rye
Appl. Opt. 39(33) 6086-6096 (2000)

SNR enhancement with a non-local means image-denoising method for a Φ-OTDR system

Junchan Li, Yu Wang, Lin Xiao, Xin Liu, and Baoquan Jin
Appl. Opt. 62(9) 2283-2291 (2023)

Hybrid B-OTDR/Φ-OTDR for multi-parameter measurement from a single end of fiber

Xiao Zhou, Feng Wang, Zhen Liu, Yanqing Lu, Chengyu Yang, Yixin Zhang, Liyang Shao, and Xuping Zhang
Opt. Express 30(16) 29117-29127 (2022)