Optical frequency domain reflectometer image signal denoising method and measuring method based on deconvolution

Abstract

The invention provides an optical frequency domain reflectometer image signal denoising method and measurement method based on deconvolution. The denoising method comprises the following steps: converting reference and measurement signals of an OFDR (Optical Frequency Domain Reflectometer) into distance domain reference signals and measurement signals by adopting FFT (Fast Fourier Transform); dividing a scattering medium for measurement into a plurality of sections according to the spatial resolution of the OFDR, and converting distance domain reference and measurement signals of each section into time domain reference and measurement signals by adopting STFT (Short Time Fourier Transform); calculating a cross-correlation spectrum curve of each segment of corresponding time domain reference and measurement signals to obtain a cross-correlation two-dimensional image; autocorrelation is carried out on the time domain reference signal of each segment, and an autocorrelation two-dimensional image is obtained; taking a main lobe, corresponding to a cross-correlation peak, of each section in the cross-correlation two-dimensional image as a wavelength interception range of the section, and intercepting an image located in the wavelength interception range of the section from each section of the self-correlation two-dimensional image as a point spread function (PSF); and de-noising the cross-correlation two-dimensional image by adopting deconvolution according to the PSF. According to the invention, the problem that the OFDR cannot carry out image signal denoising based on deconvolution is solved.

Description

Technical Field

[0001] The invention belongs to the field of optical frequency domain reflectometer signal denoising, and in particular relates to an optical frequency domain reflectometer image signal denoising method and a measurement method based on deconvolution. Background Art

[0002] Optical frequency domain reflectometry (OFDR) has high spatial resolution, sensitivity and immunity to electromagnetic interference, making it widely used in structural health monitoring, shape sensing and industrial monitoring. OFDR technology was first proposed by M. Eickhoff in 1981. In 1998, Froggatt and his colleagues studied Rayleigh scattering in optical fibers and regarded it as a weak Bragg grating with random periodicity. They found that by calculating the changes in the Rayleigh scattering spectrum before and after the strain was applied, the distributed static strain could be determined by cross-correlation. Therefore, cross-correlation measures the shift of the Rayleigh backscattering spectrum to determine the strain and has been widely adopted in OFDR sensing systems. Achieving high spatial resolution is a key reason for choosing an OFDR system. However, improving spatial resolution will bring various noise challenges to the cross-correlation method. These noises come from the system itself, environmental interference and signal processing, which manifest as distortion, broadening and increase of side lobes of the cross-correlation peak, ultimately leading to peak detection errors.

[0003] To address these noise challenges, image processing technology has become an effective solution. Soto and his colleagues first introduced image denoising technology in Brillouin optical time domain analysis, successfully eliminating the noise of fiber optic sensors and improving sensing performance. Similar methods have also been applied to phase-sensitive optical time domain reflectometry and Raman distributed temperature sensing systems, achieving remarkable results. These successful applications have promoted the in-depth research and widespread application of image denoising technology in OFDR systems.

[0004] In recent years, a variety of image denoising techniques have been applied to OFDR sensing systems to improve performance. The denoising effect is usually proportional to the complexity of the denoising algorithm. Methods such as non-local mean wavelet denoising, block matching 3D, non-local Haar transform, independent component analysis and convolutional neural network are constantly being reported. These techniques use the high similarity and redundancy of multi-dimensional information measured by distributed fiber optic sensors to enhance the signal-to-noise ratio (SNR) of the system, thereby improving measurement accuracy.

[0005] Wiener deconvolution is a classic frequency domain image denoising method that can flexibly adapt to various noise and blur conditions. By considering the combined effects of noise and blur in the frequency domain and using statistical models, Wiener deconvolution can effectively denoise and recover signal details under different conditions. Compared with other denoising algorithms, it is more adaptable and has better signal recovery capabilities, showing a wide range of application potential. For example, in the Brillouin optical time-domain reflectometer (BOTDR) system, the Wiener deconvolution method has been successfully applied to improve the spatial resolution. Its point spread function (PSF) is obtained by the two-dimensional convolution of the pulse envelope and the short-time Fourier transform window function, which is similar to the convolution of the impulse response in the time domain and the frequency domain.

[0006] However, unlike the pulsed light source in the BOTDR system, the OFDR system uses a swept frequency light source as input and cannot construct the PSF by pulse convolution in the time domain, so the method for constructing its PSF is still unclear. In view of this, the effective application of Wiener deconvolution in OFDR systems has not been reported. The challenge is how to obtain an effective PSF to minimize the loss of spatial resolution during image processing. Summary of the invention

[0007] The present invention provides an optical frequency domain reflectometer image signal denoising method and a measurement method based on deconvolution, so as to solve the problem that the current optical frequency domain reflectometer OFDR cannot denoise its image signal based on deconvolution.

[0008] According to a first aspect of an embodiment of the present invention, a method for denoising an optical frequency domain reflectometer image signal based on deconvolution is provided, comprising: step S100, collecting a reference signal and a measurement signal of an optical frequency domain reflectometer OFDR, performing fast Fourier transform FFT on the reference signal and the measurement signal respectively, to obtain a distance domain reference signal and a measurement signal;

[0009] Step S200, dividing the measurement scattering medium in the OFDR into multiple segments according to the spatial resolution of the OFDR, and for each segment, using short-time Fourier transform (STFT) to process the distance domain reference signal and the measurement signal of the segment respectively, to obtain the time domain reference signal and the measurement signal of the segment;

[0010] Step S300: for each segment, calculate the cross-correlation spectrum curve of the time domain reference signal and the measurement signal corresponding to the segment, arrange the cross-correlation spectrum curves corresponding to each segment in sequence along the scattering medium, and obtain a cross-correlation two-dimensional image, wherein two axes of the cross-correlation two-dimensional image are the distance of the scattering medium and the color gradient value used to represent the wavelength;

[0011] Step S400: arranging the time domain reference signals corresponding to the segments in step S200 in sequence along the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, and performing autocorrelation on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance of the scattering medium and the color gradient value used to represent the wavelength;

[0012] Step S500, intercepting the point spread function PSF: for the cross-correlation two-dimensional image, the main lobe of each segment corresponding to the cross-correlation peak is used as the wavelength interception range of the segment, and an image located in the wavelength interception range corresponding to the segment is intercepted from each segment of the autocorrelation two-dimensional image as the point spread function PSF;

[0013] Step S600: According to the point spread function PSF, a deconvolution algorithm is used to perform denoising on the cross-correlated two-dimensional image to obtain a denoised two-dimensional image signal.

[0014] In an optional implementation, step S300 specifically includes:

[0015] When the scattering medium is arranged in a straight line, for each section, a cross-correlation spectrum curve of a time domain reference signal and a measurement signal corresponding to the section is calculated, and the cross-correlation spectrum curves corresponding to each section are arranged in sequence along the arrangement path of the scattering medium to obtain a cross-correlation two-dimensional curve, and the cross-correlation two-dimensional curve is converted into a cross-correlation two-dimensional image, wherein two axes of the cross-correlation two-dimensional image are respectively a distance on the arrangement path of the scattering medium and a color gradient value used to represent the wavelength size;

[0016] When the scattering medium is arranged in a non-linear manner, for each segment, a cross-correlation spectrum curve of a time domain reference signal and a measurement signal corresponding to the segment is calculated, and the cross-correlation spectrum curves corresponding to each segment are arranged in sequence along the arrangement path of the scattering medium to obtain a cross-correlation three-dimensional curve;

[0017] The cross-correlation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall layout direction of the scattering medium to form a cross-correlation two-dimensional image. The two axes of the cross-correlation two-dimensional image are the distance in the overall layout direction of the scattering medium and the color gradient value used to represent the wavelength size.

[0018] In another optional implementation, the step S400 specifically includes:

[0019] When the scattering medium is arranged in a straight line, the time domain reference signals corresponding to the segments in step S200 are arranged in sequence along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation two-dimensional curve, and the autocorrelation two-dimensional curve is converted into an autocorrelation two-dimensional image, where two axes of the autocorrelation two-dimensional image are respectively the distance on the arrangement path of the scattering medium and a color gradient value used to represent the wavelength size.

[0020] When the scattering medium is arranged in a non-linear manner, the time domain reference signals corresponding to the segments in step S200 are sequentially arranged along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, and autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation three-dimensional curve; the autocorrelation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to form an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance in the overall arrangement direction of the scattering medium and a color gradient value used to represent the wavelength size.

[0021] In another optional implementation, in step S300, arranging the cross-correlation spectrum curves corresponding to each segment in sequence includes: arranging the cross-correlation peaks in the cross-correlation spectrum curves corresponding to each segment in sequence.

[0022] In another optional implementation, in step S600, the deconvolution algorithm is a Wiener deconvolution algorithm.

[0023] According to a second aspect of an embodiment of the present invention, there is provided a deconvolution-based optical frequency domain reflectometry measurement method, comprising:

[0024] Step S1, using the above-mentioned deconvolution-based optical frequency domain reflectometer image signal denoising method to denoise the image signal of the optical frequency domain reflectometer to obtain a denoised two-dimensional image signal;

[0025] Step S2, converting the denoised two-dimensional image signal into a wavelength peak shift curve;

[0026] Step S3: determining the parameters to be measured of each segment on the scattering medium according to the wavelength peak shift of each segment in the wavelength peak shift curve.

[0027] In an optional implementation, step S2 specifically includes:

[0028] When the scattering medium is arranged in a straight line, the denoised two-dimensional image signal is converted into a two-dimensional wavelength peak shift curve;

[0029] When the scattering medium is arranged in a non-linear manner, the denoised two-dimensional image signal is converted into a three-dimensional image signal according to the three-dimensional positional relationship between each segment in the denoised two-dimensional image signal and the scattering medium; and the three-dimensional image signal is converted into a three-dimensional wavelength peak shift curve.

[0030] In another optional implementation, the parameter to be measured is strain or temperature.

[0031] The beneficial effects of the present invention are:

[0032] 1. The present invention has efficient noise reduction performance: by taking the main lobe of each segment of the cross-correlation peak corresponding to the cross-correlation two-dimensional image as the wavelength interception range of the segment, intercepting the image within the wavelength interception range corresponding to the segment from each segment of the autocorrelation two-dimensional image, and using the point spread function PSF and applying the deconvolution algorithm, the noise interference in the OFDR system can be effectively reduced, the signal quality is improved, and the strain or temperature change in the optical fiber can still be accurately detected in a high-noise environment; the present invention has spatial resolution: compared with traditional image denoising methods, deconvolution can better restore signal details, avoid signal peak distortion caused by noise, and significantly improve the spatial resolution of the system, which provides strong support for the application of OFDR systems in small-scale, complex strain or temperature change environments; the present invention has flexible adaptability: the deconvolution algorithm has strong adaptability, can work efficiently under different noise and fuzzy conditions, and is suitable for multiple OFDR sensing applications in complex environments, especially in the case of environmental interference or large system noise, can be flexibly adjusted to obtain the best signal processing effect; the present invention has the characteristic of strong signal recovery ability: compared with other denoising algorithms, the deconvolution algorithm not only performs well in noise reduction, but also can effectively restore the signal peak blurred by noise. This recovery ability is particularly important in OFDR systems and can ensure the accuracy of detection results in high-precision strain sensing; the present invention has broad application potential: the method lays the foundation for realizing high-performance and high-precision applications of OFDR systems, and has a wide range of application prospects in industrial and research fields, especially in structural health monitoring, shape sensing and other distributed sensing applications with high spatial resolution; thus, the present invention provides an efficient, accurate and flexible solution for image denoising of OFDR systems, significantly improving the overall performance of OFDR systems;

[0033] 2. The present invention is directed to scattering media arranged non-linearly. When acquiring a cross-correlation two-dimensional image, the cross-correlation curves corresponding to each segment are first arranged in sequence along the scattering medium arrangement path to obtain a cross-correlation three-dimensional image. Then, the cross-correlation three-dimensional image is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to obtain a cross-correlation two-dimensional image. When acquiring an autocorrelation two-dimensional image, the time domain reference signals corresponding to each segment in step S200 are first arranged in sequence along the scattering medium arrangement path to obtain a time domain reference signal corresponding to the scattering medium. Then, the time domain reference signals of the scattering medium are cross-correlated to obtain an autocorrelation three-dimensional curve. Then, the autocorrelation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to form an autocorrelation two-dimensional image. It can be seen that even for a scattering medium arranged nonlinearly, the present invention can perform denoising processing on its image signal based on the deconvolution method.

[0034] 3. When arranging the cross-correlation spectrum curve, the present invention arranges the cross-correlation peaks in the cross-correlation spectrum curve in sequence, thereby removing part of the noise and improving the signal-to-noise ratio of the cross-correlation two-dimensional image;

[0035] 4. The present invention selects the main lobe of each segment of the cross-correlation peak corresponding to the cross-correlation two-dimensional image as the wavelength cut-off range of the segment, which not only covers the main features of the cross-correlation two-dimensional image, but also avoids over-expansion; thereafter, based on the wavelength cut-off range, an image within the wavelength cut-off range is cut from each segment of the autocorrelation two-dimensional image as the point spread function PSF, which can effectively construct a PSF that can efficiently remove noise, while retaining valuable information and improving measurement accuracy;

[0036] 5. The present invention performs denoising on the image signal of the optical frequency domain reflectometer (OFDR) based on a deconvolution algorithm, making full use of the similarity and redundancy of multidimensional data in the OFDR system, reducing the impact of noise and enhancing signal quality. The present invention greatly improves the spatial resolution and signal detection accuracy of the OFDR system measurement, and effectively improves the distributed strain sensing performance of the OFDR system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of an embodiment of the optical frequency domain reflectometer image signal denoising method based on deconvolution of the present invention;

[0038] Figure 2 It is a principle diagram of the optical frequency domain reflectometer image signal denoising method based on deconvolution of the present invention;

[0039] Figure 3 This is a comparison chart of the denoising effects of the present invention and traditional Gaussian filtering;

[0040] Figure 4The present invention is used to test the result of denoising at a high spatial resolution of 0.2 mm;

[0041] Figure 5 It is a flow chart of an embodiment of the optical frequency domain reflectometer measurement method based on deconvolution of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention and to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0043] In the description of the present invention, unless otherwise specified and limited, it should be noted that the term "connection" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two elements. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0044] See also Figure 1 , is a flow chart of an embodiment of the optical frequency domain reflectometer image signal denoising method based on deconvolution of the present invention. Figure 2 As shown, the optical frequency domain reflectometer image signal denoising method based on deconvolution may include the following steps:

[0045] Step S100, collect the reference signal and measurement signal of the optical frequency domain reflectometer OFDR, perform fast Fourier transform FFT on the reference signal and the measurement signal, respectively, and obtain the distance domain reference signal and the measurement signal. The reference signal is a signal collected when no event occurs in the scattering medium used for measurement in the OFDR, and the measurement signal is a signal collected when an event occurs in the scattering medium used for measurement in the OFDR, and the event may be a phenomenon of spectral peak shift caused by stress or temperature. The signal collected by the optical frequency domain reflectometer OFDR is a frequency domain signal, and the frequency domain signal is converted into a distance domain signal through FFT processing. The collected reference signal and measurement signal may be Rayleigh scattered light reflected back by the scattering medium, and the scattering medium may be a distributed sensing optical fiber.

[0046] Step S200: Divide the measurement scattering medium in the OFDR into multiple segments according to the spatial resolution of the OFDR. For each segment, use short-time Fourier transform (STFT) to process the distance domain reference signal and the measurement signal of the segment respectively to obtain the time domain reference signal and the measurement signal of the segment.

[0047] Step S300: for each segment, calculate the cross-correlation spectrum curve of the time domain reference signal and the measurement signal corresponding to the segment, arrange the cross-correlation spectrum curves corresponding to each segment in sequence along the scattering medium, and obtain a cross-correlation two-dimensional image, wherein the two axes of the cross-correlation two-dimensional image are the distance of the scattering medium and the color gradient value used to represent the wavelength size.

[0048] Obviously, the cross-correlation 2D data structure of each segment of the scattering medium contains repeated data signal patterns, which can be effectively used for noise reduction. Given that OFDR measurement data contains a large amount of high similarity and redundant information, 2D images can be denoised by image processing. Compared with traditional one-dimensional filtering methods, image processing is more effective in noise reduction because it can utilize all data signal patterns present in the 2D measurement data. After image denoising, the quality of the cross-correlation curve is improved, thereby improving the detection accuracy of the spectral shift peak caused by strain / temperature changes.

[0049] The step S300 may specifically include: when the scattering medium is arranged in a straight line, for each segment, calculating a cross-correlation spectrum curve between a time domain reference signal and a measurement signal corresponding to the segment, arranging the cross-correlation spectrum curves corresponding to each segment in sequence along the arrangement path of the scattering medium to obtain a cross-correlation two-dimensional curve, converting the cross-correlation two-dimensional curve into a cross-correlation two-dimensional image, wherein two axes of the cross-correlation two-dimensional image are respectively a distance on the arrangement path of the scattering medium and a color gradient value used to represent a wavelength;

[0050] When the scattering medium is arranged in a non-linear manner, for each segment, the cross-correlation spectrum curve of the time domain reference signal and the measurement signal corresponding to the segment is calculated, and the cross-correlation spectrum curves corresponding to each segment are arranged in sequence along the scattering medium to obtain a cross-correlation three-dimensional curve; perpendicular to the overall arrangement direction of the scattering medium, the cross-correlation three-dimensional curve is projected onto a two-dimensional plane to form a cross-correlation two-dimensional image, and the two axes of the cross-correlation two-dimensional image are the distance in the overall arrangement direction of the scattering medium and the color gradient value used to represent the wavelength size. In the step S300, the cross-correlation spectrum curves corresponding to each segment are arranged in sequence, which may include: the cross-correlation peaks in the cross-correlation spectrum curves corresponding to each segment are arranged in sequence. When arranging the cross-correlation spectrum curves, the cross-correlation peaks in the cross-correlation spectrum curves are arranged in sequence, so that a part of the noise is removed and the signal-to-noise ratio of the cross-correlation two-dimensional image is improved.

[0051] Step S400: arranging the time domain reference signals corresponding to the segments in step S200 in sequence along the scattering medium to obtain the time domain reference signals corresponding to the scattering medium, and performing autocorrelation on the time domain reference signals corresponding to the scattering medium to obtain an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance of the scattering medium and the color gradient value used to represent the wavelength.

[0052] The step S400 may specifically include:

[0053] When the scattering medium is arranged in a straight line, the time domain reference signals corresponding to the segments in step S200 are arranged in sequence along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation two-dimensional curve, and the autocorrelation two-dimensional curve is converted into an autocorrelation two-dimensional image, where two axes of the autocorrelation two-dimensional image are respectively the distance on the arrangement path of the scattering medium and a color gradient value used to represent the wavelength size.

[0054] When the scattering medium is arranged in a non-linear manner, the time domain reference signals corresponding to the segments in step S200 are sequentially arranged along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, and autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation three-dimensional curve; the autocorrelation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to form an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance in the overall arrangement direction of the scattering medium and a color gradient value used to represent the wavelength size.

[0055] The present invention is directed to scattering media arranged non-linearly. When acquiring a cross-correlation two-dimensional image, the cross-correlation curves corresponding to each segment are first arranged in sequence along the scattering medium arrangement path to obtain a cross-correlation three-dimensional image. Then, the cross-correlation three-dimensional image is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to obtain a cross-correlation two-dimensional image. When acquiring an autocorrelation two-dimensional image, the time domain reference signals corresponding to each segment in step S200 are first arranged in sequence along the scattering medium arrangement path to obtain a time domain reference signal corresponding to the scattering medium. Then, the time domain reference signals of the scattering medium are cross-correlated to obtain an autocorrelation three-dimensional curve. Then, the autocorrelation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to form an autocorrelation two-dimensional image. It can be seen that even for a scattering medium arranged nonlinearly, the present invention can perform denoising processing on its image signal based on the deconvolution method.

[0056] Step S500, extracting the point spread function PSF: for the cross-correlation two-dimensional image, the main lobe of each segment corresponding to the cross-correlation peak is used as the wavelength cut-off range of the segment, and an image located within the wavelength cut-off range corresponding to the segment is extracted from each segment of the autocorrelation two-dimensional image as the point spread function PSF.

[0057] In this step, deconvolution is often used for image noise reduction, and the wavelength peak offset along the optical fiber is obtained from the noise-reduced image. For the OFDR system, the most critical step is to construct the PSF of the point spread function. For the optical time domain reflectometer (OTDR) system, the detection light source is a pulsed light, and the detected signal is the convolution of the pulse envelope and the system impulse response. In contrast, since OFDR uses a swept light source for detection instead of a pulsed light source, it becomes more difficult to represent the process as a convolution in the spatial domain. In the spectral demodulation method of OFDR, the ideal signal should be infinitely long, but the window truncation of the signal causes spectral leakage, which blurs the quality of the autocorrelation peak in the wavelength domain. In the BOTDR system, the PSF is the convolution of the pulse envelope and the window function. Similarly, in the OFDR system, the PSF can be defined as the window function convolution of the reference signal and the measurement signal. When the reference signal and the measurement signal are the same, the PSF of the system is more reliable. Therefore, the autocorrelation function can be used as the PSF of the OFDR system.

[0058] The cross-correlation peak corresponding to each section in the cross-correlation two-dimensional image includes the main lobe and the side lobes on both sides of the main lobe, so the main lobe of each section corresponding to the cross-correlation peak can be accurately determined. When selecting the wavelength interception range, if it is too large, it will cause the peak of the image signal after denoising to be broadened and blurred, thereby reducing the spatial resolution and wavelength accuracy. The present invention selects the main lobe of the cross-correlation peak corresponding to each section in the cross-correlation two-dimensional image as the wavelength interception range of the section, which not only covers the main features of the cross-correlation two-dimensional image, but also avoids over-expansion; thereafter, based on the wavelength interception range, the image within the wavelength interception range is intercepted from each section of the autocorrelation two-dimensional image as the point spread function PSF, which can effectively construct a PSF that can efficiently remove noise, while retaining valuable information and improving measurement accuracy.

[0059] Step S600: According to the point spread function PSF, a deconvolution algorithm is used to perform denoising on the cross-correlated two-dimensional image to obtain a denoised two-dimensional image signal. The deconvolution algorithm may be a Wiener deconvolution algorithm.

[0060] It can be seen from the above embodiments that the present invention has efficient noise reduction performance: by taking the main lobe of each corresponding cross-correlation peak in the cross-correlation two-dimensional image as the wavelength cut-off range of the segment, cutting out the image within the wavelength cut-off range corresponding to the segment from each segment of the autocorrelation two-dimensional image, and using the point spread function PSF and applying the deconvolution algorithm, the noise interference in the OFDR system can be effectively reduced, the signal quality is improved, and the strain or temperature change in the optical fiber can still be accurately detected in a high-noise environment; the present invention has spatial resolution: compared with traditional image denoising methods, deconvolution can better restore signal details, avoid signal peak distortion caused by noise, and significantly improve the spatial resolution of the system, which provides strong support for the application of OFDR systems in small-scale, complex strain or temperature change environments; the present invention has flexible adaptability: the deconvolution algorithm has strong adaptability, can work efficiently under different noise and fuzzy conditions, and is suitable for The invention can be used for OFDR sensing applications in various complex environments, and can be flexibly adjusted to obtain the best signal processing effect, especially in the case of environmental interference or large system noise. The invention has the characteristic of strong signal recovery ability: compared with other denoising algorithms, the deconvolution algorithm not only performs well in noise reduction, but also can effectively restore the signal peak value blurred by noise. This recovery ability is particularly important in OFDR system, and can ensure the accuracy of detection results in high-precision strain sensing. The invention has broad application potential: the method lays the foundation for realizing high-performance and high-precision applications of OFDR system, and has a wide range of application prospects in industrial and research fields, especially in structural health monitoring, shape sensing and other distributed sensing applications with high spatial resolution. Therefore, the invention provides an efficient, accurate and flexible solution for image denoising of OFDR system, which significantly improves the overall performance of OFDR system.

[0061] Figure 3 This is a comparison chart of the denoising effect of the present invention and the traditional Gaussian filtering, where Figure 3 (a) is the strain gradient information before denoising, Figure 3 (b) is the denoising effect of Gaussian filtering. Figure 3 (c) is the deconvolution denoising effect of the present invention. It can be seen from the figure that the denoising effect of the present invention is better.

[0062] Figure 4 The present invention is used to test the result of denoising at a high spatial resolution of 0.2 mm, where Figure 4 (a), (c), and (e) show the signals before denoising. Figure 4 (b), (d) and (f) show the denoised signals, from which it can be seen that the denoising effect of the present invention is excellent.

[0063] In addition, the present invention also provides an optical frequency domain reflectometry measurement method based on deconvolution, which may include the following steps:

[0064] Step S1: using the above-mentioned deconvolution-based optical frequency domain reflectometry image signal denoising method to denoise the image signal of the optical frequency domain reflectometry to obtain a denoised two-dimensional image signal.

[0065] Step S2: converting the denoised two-dimensional image signal into a wavelength peak shift curve.

[0066] The step S2 may specifically include: when the scattering medium is arranged in a straight line, converting the denoised two-dimensional image signal into a two-dimensional wavelength peak shift curve;

[0067] When the scattering medium is arranged in a non-linear manner, the denoised two-dimensional image signal is converted into a three-dimensional image signal according to the three-dimensional positional relationship between each segment in the denoised two-dimensional image signal and the scattering medium; and the three-dimensional image signal is converted into a three-dimensional wavelength peak shift curve.

[0068] Step S3: Determine the measured parameter of each segment on the scattering medium according to the wavelength peak shift of each segment in the wavelength peak shift curve. The measured parameter may be strain or temperature. Each segment in the wavelength peak shift curve corresponds to a corresponding segment on the scattering medium.

[0069] It can be seen from the above embodiments that the present invention performs denoising processing on the image signal of the optical frequency domain reflectometer OFDR based on the deconvolution algorithm, fully utilizes the similarity and redundancy of multidimensional data in the OFDR system, reduces the influence of noise, and enhances the signal quality. The present invention greatly improves the spatial resolution and signal detection accuracy of the OFDR system measurement, and effectively improves the distributed strain sensing performance of the OFDR system.

[0070] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0071] It will be appreciated that the invention is not limited to the precise construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from its scope. The scope of the invention is governed solely by the appended claims.

Claims

1. A method for denoising an optical frequency domain reflectometer image signal based on deconvolution, characterized in that: include: Step S100, collecting a reference signal and a measurement signal of an optical frequency domain reflectometer (OFDR), performing fast Fourier transform (FFT) on the reference signal and the measurement signal, respectively, to obtain a distance domain reference signal and a measurement signal; Step S200, dividing the measurement scattering medium in the OFDR into multiple segments according to the spatial resolution of the OFDR, and for each segment, using short-time Fourier transform (STFT) to process the distance domain reference signal and the measurement signal of the segment respectively, to obtain the time domain reference signal and the measurement signal of the segment; Step S300: for each segment, calculate the cross-correlation spectrum curve of the time domain reference signal and the measurement signal corresponding to the segment, arrange the cross-correlation spectrum curves corresponding to each segment in sequence along the scattering medium, and obtain a cross-correlation two-dimensional image, wherein two axes of the cross-correlation two-dimensional image are the distance of the scattering medium and the color gradient value used to represent the wavelength; Step S400: arranging the time domain reference signals corresponding to the segments in step S200 in sequence along the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, and performing autocorrelation on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance of the scattering medium and the color gradient value used to represent the wavelength; Step S500, intercepting the point spread function PSF: for the cross-correlation two-dimensional image, the main lobe of each segment corresponding to the cross-correlation peak is used as the wavelength interception range of the segment, and an image located in the wavelength interception range corresponding to the segment is intercepted from each segment of the autocorrelation two-dimensional image as the point spread function PSF; Step S600: According to the point spread function PSF, a deconvolution algorithm is used to perform denoising on the cross-correlated two-dimensional image to obtain a denoised two-dimensional image signal.

2. The method for denoising an optical frequency domain reflectometer image signal based on deconvolution according to claim 1, characterized in that: The step S300 specifically includes: When the scattering medium is arranged in a straight line, for each section, a cross-correlation spectrum curve of a time domain reference signal and a measurement signal corresponding to the section is calculated, and the cross-correlation spectrum curves corresponding to each section are arranged in sequence along the arrangement path of the scattering medium to obtain a cross-correlation two-dimensional curve, and the cross-correlation two-dimensional curve is converted into a cross-correlation two-dimensional image, wherein two axes of the cross-correlation two-dimensional image are respectively a distance on the arrangement path of the scattering medium and a color gradient value used to represent the wavelength size; When the scattering medium is arranged in a non-linear manner, for each segment, a cross-correlation spectrum curve of a time domain reference signal and a measurement signal corresponding to the segment is calculated, and the cross-correlation spectrum curves corresponding to each segment are arranged in sequence along the arrangement path of the scattering medium to obtain a cross-correlation three-dimensional curve; The cross-correlation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall layout direction of the scattering medium to form a cross-correlation two-dimensional image. The two axes of the cross-correlation two-dimensional image are the distance in the overall layout direction of the scattering medium and the color gradient value used to represent the wavelength size.

3. The method for denoising an optical frequency domain reflectometry image signal based on deconvolution according to claim 1 or 2, characterized in that: The step S400 specifically includes: When the scattering medium is arranged in a straight line, the time domain reference signals corresponding to the segments in step S200 are arranged in sequence along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation two-dimensional curve, and the autocorrelation two-dimensional curve is converted into an autocorrelation two-dimensional image, where two axes of the autocorrelation two-dimensional image are respectively the distance on the arrangement path of the scattering medium and a color gradient value used to represent the wavelength size. When the scattering medium is arranged in a non-linear manner, the time domain reference signals corresponding to the segments in step S200 are sequentially arranged along the arrangement path of the scattering medium to obtain the time domain reference signal corresponding to the scattering medium, and autocorrelation is performed on the time domain reference signal corresponding to the scattering medium to obtain an autocorrelation three-dimensional curve; the autocorrelation three-dimensional curve is projected onto a two-dimensional plane perpendicular to the overall arrangement direction of the scattering medium to form an autocorrelation two-dimensional image, wherein two axes of the autocorrelation two-dimensional image are respectively the distance in the overall arrangement direction of the scattering medium and a color gradient value used to represent the wavelength size.

4. The method for denoising an optical frequency domain reflectometry image signal based on deconvolution according to claim 1 or 2, characterized in that: In the step S300, sequentially arranging the cross-correlation spectrum curves corresponding to each segment includes: sequentially arranging the cross-correlation peaks in the cross-correlation spectrum curves corresponding to each segment.

5. The method for denoising an optical frequency domain reflectometry image signal based on deconvolution according to claim 1, characterized in that: In step S600, the deconvolution algorithm is a Wiener deconvolution algorithm.

6. A method for measuring optical frequency domain reflectometry based on deconvolution, characterized in that: include: Step S1, using the optical frequency domain reflectometer image signal denoising method based on deconvolution as described in any one of claims 1 to 5 to denoise the image signal of the optical frequency domain reflectometer to obtain a denoised two-dimensional image signal; Step S2, converting the denoised two-dimensional image signal into a wavelength peak shift curve; Step S3: determining the parameters to be measured of each segment on the scattering medium according to the wavelength peak shift of each segment in the wavelength peak shift curve.

7. The optical frequency domain reflectometry measurement method based on deconvolution according to claim 6, characterized in that: The step S2 specifically includes: When the scattering medium is arranged in a straight line, the denoised two-dimensional image signal is converted into a two-dimensional wavelength peak shift curve; When the scattering medium is arranged in a non-linear manner, the denoised two-dimensional image signal is converted into a three-dimensional image signal according to the three-dimensional positional relationship between each segment in the denoised two-dimensional image signal and the scattering medium; and the three-dimensional image signal is converted into a three-dimensional wavelength peak shift curve.

8. The optical frequency domain reflectometry measurement method based on deconvolution according to claim 6, characterized in that: The parameter to be measured is strain or temperature.

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