###### Abstract
Correlation methods [32; 33], wavelet filtering [34], neural networks [35; 36], and many other approaches are among them. However, since  
Figure 1: An example of the BOTDA/BOTDR type systems applications in monitoring the condition of a civil aircraft [26; 27; 28]. most types of distributed fiber optic sensors record two-dimensional and three-dimensional data, where redundancy and similarity of information at different points of the optical fiber can be effectively utilized, methods similar to image processing algorithms [37; 38; 39] have become especially popular among researchers. For instance, in the study [40], a multi-threshold discrete 2D wavelet is used to improve the signal-to-noise ratio of BOTDA. The authors of this work demonstrate the BOTDA operating with a 154.42 km long fiber line and 40 ns pump pulses, while the measurement error is reduced from \(\pm\)1.52 MHz to \(\pm\)1.03 MHz. In another study, the authors [41] present an experimental method for enhancing the accuracy of BOTDA/BOTDR measurements by leveraging the similarities and redundancies inherent in the 3D data of BGS profiles. The scientists employed image processing, wavelet analysis, and non-local means (NLM) in the time and frequency domains, respectively, to demonstrate a significant increase in the signal-to-noise ratio and measurement contrast. While these approaches yielded promising results in practice, the use of wavelet analysis necessitated the identification of a basis function for each study, which entailed additional time costs. In [42], the authors demonstrate the temperature measurement with an error of \(\pm\)0.70 \({}^{\circ}\)C and a spatial resolution of 8 m over a sensor length of more than 150 km using optical pulse coding and NLM image processing. However, this approach requires modification of the probing regimes, which excludes the post-processing regime of data obtained on the instrument probing the sensor in a standard way (with a rectangular pulse). In [43], the authors analyze linear and nonlinear approaches to processing two-dimensional data arrays to remove noise from Brillouin analyzer readings. They study the effect of filter parameters on noise-free data using the NLM method. The proposed method is also interesting for its results; however, according to the authors, its application is optimal for long lines and high-frequency resolution. In [44], the authors investigate the noise reduction of images obtained based on BGS using convolutional neural networks to enhance the accuracy of deformation measurements. It is demonstrated that denoising BGS images in the time domain can more effectively detect rapidly changing strain (vibration) compared to denoising BGS images along the fiber length. In order to enhance the speed of BOTDA due to fewer measurements and lower sampling rates in the time domain, the authors of [45] proposed a method based on a generative adversarial network to reconstruct super-resolution BGS from its analog with a fourfold reduction in resolution. The results demonstrated that 75% of the measurements were reduced, and the resolution in the spatial and frequency domains was increased fourfold, which led to a notable increase in measurement accuracy. It is indubitable that neural network algorithms and machine learning methods are indispensable components of contemporary signal processing. However, it is crucial to acknowledge that a significant shortcoming of this approach is the accumulation of data for training the system, which in certain instances necessitates repetition when modifying the measurement parameters or modifying the setup. The BOTDA presented in [46] based on an optical chirp chain has demonstrated that it can perform rapid measurements over extended distances. In their work, the authors suppress polarization fading by separating information about different polarization states. They further increase the signal-to-noise ratio by employing image processing methods, which avoids averaging BOTDA traces.