Methods, systems, and media of spectral bow correction

Abstract

The application provides a spectrum bending correction method, system and medium, relates to the fields of spectral imagers and satellite remote sensing technology, and comprises the following steps: S1, selecting a solar spectrum image as sample data for spectrum bending correction, and performing curve fitting on the sample data; S2, calculating the bending degree of each column of a to-be-corrected image according to the fitted curve; and S3, correcting each column of the to-be-corrected image by using the time shift property of Fourier transform according to the bending degree of each column, completing the correction of all column data, and obtaining complete corrected spectrum image data. The application can not need to increase the design and manufacturing difficulty of a spectral element or a slit, not need complex calculation, only use the time shift property of discrete Fourier transform to process image data, and simply and intuitively complete spectrum bending correction.

Description

Technical Field

[0001] This invention relates to the fields of spectral imagers and satellite remote sensing technology, specifically to a mathematical method for spectral image curvature correction, more specifically to a method for curve fitting of spectral curvature and correction of spectral curvature using the time-shifting property of discrete Fourier transform. In particular, it relates to a method, system, and medium for spectral curvature correction. Background Technology

[0002] Pushbroom-style spectral imagers use a solid-state self-scanning method to scan and image a two-dimensional field of view using an area array detector. However, because light rays at different positions along the slit length pass through different refractive surfaces of the beam splitter, spectral curvature occurs in the spatial dimension. This spectral curvature causes spectral aliasing in the image. Therefore, correcting spectral curvature is essential and has high practical application value.

[0003] Currently, spectral curvature correction mainly employs optical and mathematical methods.

[0004] Optical methods primarily improve spectral bending effects by optimizing the design of dispersive components or slits. For example, patent CN104034419A describes a method using a combination of prisms and gratings as a dispersive device. It utilizes the characteristic that spectral lines bend in opposite directions during dispersion (grating lines bend towards the wavelength, prism lines bend towards shorter wavelengths) to correct the spectral bending at the center wavelength, resulting in a symmetrical distribution of spectral bending at other wavelengths. However, because the spectral bending produced by the grating and prism is inconsistent across the entire operating wavelength range, this structure exhibits varying degrees of spectral bending in different wavelength bands. Patent CN108663119A describes a method that uses a curved slit to compensate for the spectral bending introduced by the entire system, thereby correcting the spectral bending of spectrometers. However, the curved slit is difficult to machine, making it challenging to manufacture a qualified slit.

[0005] Mathematical methods, such as those described in "Research on Spectral Curvature Correction Method for Hyperspectral Imagers - Doctoral Dissertation by Li Lin," introduce an interpolation approach to eliminate spectral aliasing. This method resamples the radiant energy data acquired by the system based on wavelength calibration data without deviation, ensuring that the corrected output image corresponds to the input uncorrected image. Specific interpolation methods include linear interpolation, piecewise four-point cubic Lagrange interpolation, and cubic spline interpolation. The essence of interpolation is to estimate the data at unknown points using known sample values, requiring convolution of the data to be corrected with a finite-length continuous impulse response called the interpolation kernel.

[0006] This method involves convolution operations, which are computationally intensive, and the bending correction process is not intuitive and is difficult to understand.

[0007] The invention patent with publication number CN104316183A introduces a method that calculates the distances of multiple points closest to the center wavelength without deviation, calculates the weighted contribution of these points to the center wavelength without deviation, and recalculates and assigns a value to the spectral radiation energy corresponding to the center wavelength based on the weighted contribution value.

[0008] This method considers the correlation between adjacent bands, has high spectral fidelity, and is highly versatile, but the calculation and allocation of weight values ​​is complex and computationally intensive. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method, system, and medium for spectral bending correction.

[0010] According to the present invention, a method, system, and medium for spectral bending correction are provided, the solution of which is as follows:

[0011] In a first aspect, a method for spectral curvature correction is provided, the method comprising:

[0012] Step S1: Select a solar spectrum image as sample data for spectral curvature correction, and perform curve fitting on the sample data;

[0013] Step S2: Calculate the curvature of each column of the image to be corrected based on the fitted curve;

[0014] Step S3: Based on the curvature of each column, use the Fourier transform time-shift property to correct each column of the image to be corrected, and complete the correction of all column data to obtain the corrected complete spectral image data.

[0015] Preferably, step S1 includes:

[0016] Step S1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension;

[0017] Step S1.2: Find the row number of the minimum value in each column from s(m,n), and save the row number of the minimum value into a one-dimensional array y(m);

[0018] Step S1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

[0019] Preferably, step S2 includes:

[0020] Step S2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e.

[0021] Step S2.2: Read the solar spectrum image that needs to be corrected and save it into a two-dimensional array q(m,n). Create a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0022] Step S2.3: Read the i-th column (1≤i≤M) of q(m,n) and save it into a one-dimensional array x. i (n);

[0023] Step S2.4: Calculate the difference Δy between the valley values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

[0024] Preferably, step S3 includes:

[0025] Step S3.1: For x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n));

[0026] Step S3.2: Utilize the time-shift property of the Discrete Fourier Transform to transform X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is

[0027]

[0028] Step S3.3: Save the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n);

[0029] Repeat steps S2.3 to S3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

[0030] Secondly, a system for spectral bending correction is provided, the system comprising:

[0031] Module M1: Select a solar spectrum image as sample data for spectral curvature correction and perform curve fitting on the sample data;

[0032] Module M2: Calculates the curvature of each column of the image to be corrected based on the fitted curve;

[0033] Module M3: Based on the curvature of each column, the Fourier transform time-shift property is used to correct each column of the image to be corrected, and the correction of all columns of data is completed to obtain the corrected complete spectral image data.

[0034] Preferably, the module M1 includes:

[0035] Module M1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension;

[0036] Module M1.2: Find the row number of the minimum value in each column from s(m,n) and store the row number of the minimum value in a one-dimensional array y(m);

[0037] Module M1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

[0038] Preferably, the module M2 includes:

[0039] Module M2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e.

[0040] Module M2.2: Reads the solar spectrum image that needs to be corrected, saves it into a two-dimensional array q(m,n), and creates a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension;

[0041] Module M2.3: Read the i-th column (1≤i≤M) of q(m,n) and store it in a one-dimensional array x. i (n);

[0042] Module M2.4: Calculates the difference Δy between the trough values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

[0043] Preferably, the module M3 includes:

[0044] Module M3.1: for x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n));

[0045] Module M3.2: Utilizing the time-shift property of the Discrete Fourier Transform, X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is

[0046] Module M3.3: Saves the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n);

[0047] Repeat modules M2.3 to M3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

[0048] In a second aspect, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps of the method.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. Compared with other methods of spectral curvature correction, this invention does not require increasing the design and manufacturing difficulty of spectroscopic components or slits, nor does it require complex calculations. It only utilizes the time-shifting characteristics of discrete Fourier transform to process image data, correcting only the curvature as much as possible, thus completing spectral curvature correction in a very simple and intuitive way.

[0051] 2. Since ultra-high spectral resolution spectral imagers have relatively wide dispersion and small spectral aliasing, the method used in this invention ignores the influence of spectral aliasing between adjacent bands and directly performs spatial dimension correction. Attached Figure Description

[0052] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0053] Figure 1 This is a schematic diagram of the process of the present invention;

[0054] Figure 2 The fitted quadratic curve;

[0055] Figure 3 The image represents the uncorrected, curved solar spectrum, with the horizontal dimension representing the spatial dimension and the vertical dimension representing the spectral dimension.

[0056] Figure 4 The solar spectrum after curvature correction is shown, with the horizontal dimension representing the spatial dimension and the vertical dimension representing the spectral dimension. Detailed Implementation

[0057] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0058] This invention provides a method for spectral curvature correction, referring to... Figure 1 and Figure 2 As shown, the method specifically includes:

[0059] Step S1: Select a solar spectrum image as sample data for spectral curvature correction and perform curve fitting on the sample data.

[0060] Specifically, step S1, which involves curve fitting of the sample data, includes:

[0061] Step S1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0062] Step S1.2: Find the row number of the minimum value in each column from s(m,n) and save the row number of the minimum value into a one-dimensional array y(m).

[0063] Step S1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

[0064] Step S2: Refer to Figure 2 As shown, the curvature of each column of the image to be corrected is calculated based on the fitted curve.

[0065] The calculation of the curvature of each column of the image to be corrected, specifically includes the following steps in step S2:

[0066] Step S2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e.

[0067] Step S2.2: Read the solar spectrum image that needs to be corrected and save it into a two-dimensional array q(m,n). Create a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0068] Step S2.3: Read the i-th column (1≤i≤M) of q(m,n) and save it into a one-dimensional array x.i (n).

[0069] Step S2.4: Calculate the difference Δy between the valley values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

[0070] Step S3: Based on the curvature of each column, use the Fourier transform time-shift property to correct each column of the image to be corrected, and complete the correction of all column data to obtain the corrected complete spectral image data.

[0071] In step S3, the correction of each column of the image to be corrected using the time-shift property of Fourier transform specifically includes:

[0072] Step S3.1: For x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n)).

[0073] Step S3.2: Utilize the time-shift property of the Discrete Fourier Transform to transform X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is

[0074] Step S3.3: Save the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n);

[0075] Repeat steps S2.3 to S3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

[0076] The present invention also provides a system for spectral bending correction, referring to Figure 1 and Figure 2 As shown, the system includes:

[0077] Module M1: Select a solar spectrum image as sample data for spectral curvature correction and perform curve fitting on the sample data.

[0078] Module M2: Calculates the curvature of each column of the image to be corrected based on the fitted curve.

[0079] Module M3: Based on the curvature of each column, the Fourier transform time-shift property is used to correct each column of the image to be corrected, and the correction of all columns of data is completed to obtain the corrected complete spectral image data.

[0080] Module M1 includes:

[0081] Module M1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0082] Module M1.2: Find the row number of the minimum value in each column of s(m,n) and save the row number of the minimum value into a one-dimensional array y(m).

[0083] Module M1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

[0084] Module M2 includes:

[0085] Module M2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e.

[0086] Module M2.2: Reads the solar spectrum image that needs to be corrected, saves it into a two-dimensional array q(m,n), and creates a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0087] Module M2.3: Read the i-th column (1≤i≤M) of q(m,n) and store it in a one-dimensional array x. i (n).

[0088] Module M2.4: Calculates the difference Δy between the trough values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

[0089] Module M3 includes:

[0090] Module M3.1: for x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n)).

[0091] Module M3.2: Utilizing the time-shift property of the Discrete Fourier Transform, X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column.i '(n), that is

[0092] Module M3.3: Saves the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n);

[0093] Repeat modules M2.3 to M3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

[0094] The present invention will now be described in more detail.

[0095] To address the spectral curvature correction requirements of ultra-high spectral resolution spectral imagers, a method for spectral curvature correction is proposed. First, a quadratic polynomial fitting is performed on the curved spectrum; this fitted curve mathematically describes the degree of spatial curvature. Then, each column is sequentially shifted according to its degree of curvature, compensating for the exact amount of curvature. Based on the time-shifting property of Fourier transform, a time-shifted signal undergoes only a phase change after Fourier transform, while its spectral amplitude remains unchanged. Therefore, by performing a Fourier transform on each column of data in the curved spectrum, multiplying it by a phase function related to the shift, and then performing an inverse Fourier transform, the corrected value for that column is obtained. By performing this operation on each column of the curved spectral image, the spectral curvature correction for the entire image is completed. This method is simple and intuitive, and avoids complex convolution operations through Fourier transform. Because ultra-high spectral resolution spectral imagers have relatively high dispersion and low spectral aliasing, this method ignores the influence of spectral aliasing between adjacent bands, avoiding the weight calculation and redistribution process between adjacent bands.

[0096] Solar spectral images observed by the Solar Spectral Scanning Imager (Spectral resolution 0.072 Å, pixel spectral resolution 0.025 Å, observation band Ha spectral line (center wavelength 6562.8 Å) and more than 200 adjacent channels) of the Solar Dual-Ultra-Satellite Solar Spectral Scanning Imager are shown below. Figure 3 The solar spectrum image after spectral curvature correction using the time-shift property of discrete Fourier transform is shown below. Figure 4 As shown.

[0097] The method specifically includes the following steps:

[0098] (1): Select a solar spectrum image as the sample data for spectral curvature correction, read the image and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension and n is the spectral dimension.

[0099] (2): Find the row number of the minimum value in each column from s(m,n), and save the row number of the minimum value into a one-dimensional array y(m).

[0100] (3): Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

[0101] (4): Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e.

[0102] (5): Read the solar spectrum image that needs to be corrected, save it into a two-dimensional array q(m,n), and create a two-dimensional array p(m,n) to save the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension.

[0103] (6): Read the i-th column (1≤i≤M) of q(m,n) and save it into a one-dimensional array x. i (n).

[0104] (7): Calculate the difference Δy between the i-th column and the valley of the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

[0105] (8): For x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n)).

[0106] (9): Utilizing the time-shift property of the discrete Fourier transform, X i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is

[0107] (10): Save the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n).

[0108] Repeat steps (6) to (10) to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

[0109] This invention provides a method, system, and medium for spectral curvature correction, meeting the requirements of ultra-high spectral resolution spectral imagers. Compared to other spectral curvature correction methods, this invention does not require increased design or manufacturing complexity for spectroscopic components or slits, nor does it require complex calculations. It simply utilizes the time-shifting characteristics of the discrete Fourier transform to process image data, correcting only the curvature as it is, thus achieving spectral curvature correction in a very simple and intuitive way. Because ultra-high spectral resolution spectral imagers have relatively high dispersion and low spectral aliasing, this method ignores the influence of spectral aliasing between adjacent bands and directly performs spatial dimension correction.

[0110] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0111] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for spectral curvature correction, characterized in that, include: Step S1: Select a solar spectrum image as sample data for spectral curvature correction, and perform curve fitting on the sample data; Step S2: Calculate the curvature of each column of the image to be corrected based on the fitted curve; Step S3: Based on the curvature of each column, use the Fourier transform time-shift property to correct each column of the image to be corrected, and complete the correction of all column data to obtain the corrected complete spectral image data.

2. The method for spectral bending correction according to claim 1, characterized in that, Step S1 includes: Step S1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension; Step S1.2: Find the row number of the minimum value in each column from s(m,n), and save the row number of the minimum value into a one-dimensional array y(m); Step S1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

3. The method for spectral bending correction according to claim 1, characterized in that, Step S2 includes: Step S2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e. Step S2.2: Read the solar spectrum image that needs to be corrected and save it into a two-dimensional array q(m,n). Create a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension. Step S2.3: Read the i-th column (1≤i≤M) of q(m,n) and save it into a one-dimensional array x. i (n); Step S2.4: Calculate the difference Δy between the valley values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

4. The method for spectral bending correction according to claim 3, characterized in that, Step S3 includes: Step S3.1: For x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n)); Step S3.2: Utilize the time-shift property of the Discrete Fourier Transform to transform X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is Step S3.3: Save the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n); Repeat steps S2.3 to S3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

5. A system for spectral curvature correction, characterized in that, include: Module M1: Select a solar spectrum image as sample data for spectral curvature correction and perform curve fitting on the sample data; Module M2: Calculates the curvature of each column of the image to be corrected based on the fitted curve; Module M3: Based on the curvature of each column, the Fourier transform time-shift property is used to correct each column of the image to be corrected, and the correction of all columns of data is completed to obtain the corrected complete spectral image data.

6. The spectral bending correction system according to claim 5, characterized in that, The module M1 includes: Module M1.1: Select a solar spectrum image as sample data for spectral curvature correction, read the image, and save it into a two-dimensional array s(m,n), where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension; Module M1.2: Find the row number of the minimum value in each column from s(m,n) and store the row number of the minimum value in a one-dimensional array y(m); Module M1.3: Perform a quadratic polynomial fitting on y(m) to obtain y(m) = am 2 The coefficients a, b, c of +bm+c.

7. The spectral bending correction system according to claim 5, characterized in that, The module M2 includes: Module M2.1: Based on the fitted quadratic polynomial, calculate the valley value of the fitted curve, i.e. The value at that point is denoted as miny, i.e. Module M2.2: Reads the solar spectrum image that needs to be corrected, saves it into a two-dimensional array q(m,n), and creates a two-dimensional array p(m,n) to store the data after spectral curvature correction, where 1≤m≤M, 1≤n≤N, m is the spatial dimension, and n is the spectral dimension; Module M2.3: Read the i-th column (1≤i≤M) of q(m,n) and store it in a one-dimensional array x. i (n); Module M2.4: Calculates the difference Δy between the trough values ​​of the i-th column and the fitted curve based on the fitted quadratic polynomial. i =(ai) 2 +bi+c)-miny.

8. The spectral bending correction system according to claim 7, characterized in that, The module M3 includes: Module M3.1: for x i (n) Perform a discrete Fourier transform to obtain X i (k)(1≤k≤N), i.e., X i (k)=fft(x i (n)); Module M3.2: Utilizing the time-shift property of the Discrete Fourier Transform, X... i (k) and Multiply the results, perform an inverse Fourier transform, and take the real part to obtain the corrected data x for this column. i '(n), that is Module M3.3: Saves the corrected data in the i-th column to the i-th column of p(m,n), i.e., p(i,n) = x i '(n); Repeat modules M2.3 to M3.3 to complete the correction of all columns of data in q(m,n) and obtain the corrected complete spectral image data p(m,n).

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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