Abstract

Raman scattering-based distributed sensor is nowadays the most mature and consolidated technology for distributed temperature sensing at long ranges. Even though the various applied methods in recent decades have made important improvements in performance of Raman sensors, they do not thoroughly exploit the high level of correlation and similitude existing in the acquired data. In the present, we exploit a 2D image processing method called non-local means (NLM) algorithm that takes full advantage of, compared to existing methods, highly redundant texture of the 1D data obtained from distributed Raman sensors. One of the main drawbacks of the original NLM filter in distributed sensors area is to establish complete squared similarity windows for denoising the boundary pixels, corresponding to the data placed at the last row of the data matrices, which constrains the best attainable SNR values. To overcome this issue, we introduce an unprecedented approach called partial window (PW) by which we utilize partial similarity windows for denoising the boundary pixels. The PW-based NLM (PW-NLM) technique not only leads to higher levels of SNR compared with the original one, but also has no detrimental effects on the denoised data. The simulation results demonstrate the SNR improvement of 2.8 dB using the PW-based NLM filter over the original NLM filter corresponding to the 17 dB SNR boost compared with the unprocessed data.