Long Distance Distributed Strain Sensing in OFDR by BM3D-SAPCA Image Denoising

Abstract

We present a long distance distributed strain sensing in optical frequency domain reflectometry (OFDR) by shape-adaptive principal component analysis Block-Matching three-dimensional filter (BM3D-SAPCA) image denoising, which uses correlated patterns and high degree redundancy of sensing data for enhancing the performance of distributed sensing by image processing for removing noise and increasing the signal-to-noise ratio (SNR) of noisy measurements. Compared with 2D image denoising methods, BM3D-SAPCA method searches similar 2D image blocks and stacks them together in 3D arrays, which takes full advantage of the high level of similitude and redundancy contained on the multidimensional information to denoise. We find that the BM3D-SAPCA method can effectively suppresses noise aggravation along with an increasing of the sensing distance. Without modifying the hardware system of OFDR, we achieve a distributed strain sensing with a 5 cm spatial resolution, a 2 μϵ strain resolution on a 200 m all grating fiber. We compare the performance of distributed strain sensing by BM3D-SAPCA with Gaussian filter, wavelet denoising (WD) and non-local mean filter (NLM) using the same data. The mean maximal strain measurement error at loaded strain areas is reduced from 2.3791 μϵ to 0.6545 μϵ by BM3D-SAPCA. These mean errors by Gaussian filter, NLM and WD are 1.1177 μϵ, 1.6668 μϵ and 1.9721 μϵ, respectively. The mean standard deviations of strain measurement in eight repeat experiments is reduced from 1.5221 μϵ to 0.3134 μϵ after noise reduction by BM3D-SAPCA, which is 79.37% lower than the raw data. The mean standard deviations after noise reduction by Gaussian filter, NLM and WD are decreased by 68.85%, 64.24% and 14.41% respectively.